Ladies and gentlemen, the President of the United States, accompanied by Mr. C. William Verity. It's a great pleasure to welcome you to the Department of Commerce. It's been a long time since we've had a President here, and that says to us that you know as we do that Bill Verity is something special. We hope you come back often. Our 75th anniversary is next year. Maybe Bill can bring you back. Thank you for coming, sir. Thank you. Thank you. And with regard to your invitation, you mean the place is younger than I am? Well, we're here today to honor an old friend and welcome a new one. The old friend, of course, was Malcolm Baldrige. No one knows and appreciates better Max's contribution to the history of our times than you in this room. You were his colleagues for six and a half years, and the partnership between you and him was perhaps the most fruitful in the history of this department, going back even to Herbert Hoover's term here. Max had a special quality. He was direct and unassuming. He didn't stand on ceremony. He kept his eye on the big picture, but not so much that he ever took for granted those working with him. We won't see Max in the halls or in meetings anymore, but we'll remember him in our prayers. You may remember some years ago a definition of prayer that was in a movie called How Green Was My Valley. It reminded me of Max. It's when Walter Pigeon tells young Roddy McDowell, prayer is only another name for good, clean, direct thinking. When you pray, think. Think well what you're saying and make your thoughts into things that are solid. And he concluded, in that way, your prayer will have strength and that strength will become a part of your body, mind, and spirit. I wanted to come over here today to tell you that our thoughts of Max will also take a solid form in the years ahead. You see the great hall of this, one of Washington's great monumental buildings, the building in which Max did so much good work for our country. The great hall is to be renamed after Max, the Malcolm Baldrige Great Hall. Now I said we were also welcoming a new friend today, and you know who I'm talking about, your new secretary, Bill Verity. Bill has big shoes to fill and I'm confident he's just the man to fill them. You know his story, the man who successfully chartered the course of Arm Coast Steel in the 60s and 70s, the chairman of the highly successful task force on private sector initiatives, the chairman of the U.S. Chamber of Commerce, the president of the union that represents Arm Coast workers recently said of Bill, he's an honest person, he's strong, he listens to both sides, and then he gives you a fair decision. Well I can't think of any fairer or better endorsement than that. Bill, the Commerce Department is one of the class acts of the administration and so are you, welcome aboard. By the way, Bill, you may have heard questions over the years about whether departments run secretaries or secretaries run departments. Who's really in the driver's seat? Well it can be confusing, it reminds me of a story, in case you were wondering this is my way of sliding into a story. Many of you here work on east-west trade issues and I like to collect stories that I can verify that the Russian people tell among themselves, so I'm going to tell you this one. General Secretary Gorbachev seems that as part of the campaign to straighten things out there in his country, he had issued an order that everyone caught speeding or seen speeding should get a ticket no matter how important they might be. One morning he was out at his country home and realized that he was running late for a meeting that he had in the Kremlin and he went out to get in his car and told the driver to get in the back seat that he'd drive and he did and down the street he went and they passed two motorcycle policemen and the one of them took off after him and a little while later he came back and joined his companion, the other motorcycle officer and the fellow said, did you give him a ticket? And he said, no. Well he said, why not? Well he said, no, no, this was someone too important. Well he said we were told to give it no matter who it was that they were getting a ticket. No, he says not that, well he said who was it? Well he said, I don't know, I couldn't recognize him there but his driver is Gorbachev. Well, Bill will be in the driver's seat with some very important people and I mean all of you. I can't think of a department with a more noble purpose, one more central to the long-term welfare of our nation than this one. If it is true as I believe it is that trade and economic relations are the brick and mortar in the temple of world peace then your department is a department of peace. I know how many extraordinarily different functions are in the department. You're often compared to a business conglomerate but each of these activities whether it's predicting the weather, taking the census, compiling economic data or working on international trade each of these helps make America stronger in the world economic arena. Our trade policies of course must always be consistent with our national security interest. Today we know that the national interest of our country is directly challenged by the flow of advanced technologies and defense related know-how and materials to hostile nations. Your department under the leadership of Bill Verity must be ever vigilant in stopping this kind of harmful technology transfer to our adversaries. That's one of the challenges of the growing world economy. Not only challenges await us but opportunities. I believe that the birth of a truly global economy such as we're seeing happen now will mark the birth of a new age of peace. It's already marking a new era of opportunity. This month we'll set a record the longest peacetime economic expansion on record. I know you know what that means because I get many of the numbers from you and those that come from elsewhere you study with a microscope. In September the unemployment rate fell to the lowest level in this decade. A greater percentage of our working age population has been employed this year than ever before in our history. Personal and family income have risen steadily since our recovery began. The leading indicators are sending a message steady as she goes. As one national business magazine wrote recently business is holding to its faster growth track buoyed by the revival in factory output. Yes, even as some still talk about the deindustrialization of America manufacturing output has surged and exports have played a major part in this. Perhaps a few of you could take the fellows around the camera platform aside later and explain to them what I've been trying to help them understand for some time now. We've been seeing substantial growth in exports in recent months and our exports continue to stay well above their 1986 average. Exports have also been growing strongly in volume terms while import volumes are down. Emphasizing only the dollar value of the trade deficit misses some of these fundamentals. We've also made headway with our major trading partners through our improved economic policy coordination process. Indeed, I'm delighted that Secretary Baker has proposed reforming this process by adding a new commodity price indicator to assist us in reaching judgments about mutually consistent policies and performance. By the way, could you also remind those fellows in the back there that not only is American industry more competitive than ever but that since our recovery began we've created more jobs in America than Europe and Japan combined and that more than 10 million American jobs depend on imports, exports, or both. This is exactly the wrong time although there never is a right time for Congress to get on a protectionist binge. The trade bill is working its way through Capitol Hill and I know I speak for all of you when I say the administration will work with Congress to achieve responsible trade legislation but we will not support and I am prepared to veto anything that smacks of protectionism whether it's procedural in nature or overt. Protectionism destroys jobs. It destroys growth. It undermines the entire global economy and it undermines our own economy. I hope that Congress understands this and will produce a trade bill that you can be proud of and that I can sign. Let me add one other thing. I hope that Congress will keep in mind that a strong and growing global economy is the great hope of the world of the next century. More than any other department of the government you are helping our nation move into that economy of the century to come. You're helping to build a true temple of lasting world peace and too often your work is not sufficiently recognized. So let me leave you today with my profound thanks on behalf of the entire nation for your loyalty, your dedication and your hard work. Thank you. God bless you. Now I'd like to introduce Justice Sandra O'Connor who will swear in our new secretary C. William Verity, Justice O'Connor. Thank you. On the Bible and raise your right hand and repeat the oath after me. I see William Verity. I see William Verity. Do solemnly swear. Do solemnly swear. That I will support and defend. That I will support and defend. The Constitution of the United States. The Constitution of the United States. Against all enemies, foreign and domestic. Against all enemies, foreign and domestic. That I will bear true faith. That I will bear true faith. And allegiance to the same. And allegiance to the same. That I take this obligation freely. That I take this obligation freely. Without any mental reservation. Without any mental reservation. Or purpose of evasion. Or purpose of evasion. And that I will well and faithfully discharge. And that I will well and faithfully discharge. The duties of the office. on which I am about to enter so help me God so help me God thank you mr. president for putting your trust in me you Tim for your prayers which got Peggy and me here thank you senators and particularly senator Thurman who's right in the center who brought me to confirmation and commerce must lead the charge in seeking innovative ways to reduce the trade deficit so mr. president we are ready to go to work thank you mr. president for being here it's a great honor to have you in the Commerce Department


out near midfield and get tackled by Kenny Moore because if Genevich hits gets a bite. Good morning to you. It's snowing out. It's a cold, cozy league day as we like to say here at the DeMore house and you better believe that soaking up a little football, some popcorn is part of my strategy for getting ready for New York City, just trying to rest as much as I possibly can. So we're going to talk about that later but we're just soaking in our Broncos, right Joseph? Even though they're kind of even though they're kind of struggling this year. Struggling just a little bit, did a little bit. That's right, last game they won. Back to the chilly studio. It's snowing again in Denver. Usually we don't get snow in Denver until about Halloween. So we're a little early this year. I don't know if that's a sign of the winter to come but anyway we're staying warm out here in the studio. Good relaxing day with the family. It's Sunday and yes, took the Skechers Max Road 4s out for a 13 mile run today, 21k. My watch actually didn't stop anyway. It was about 650 a mile I would say was the average pace and there it is on your screen in kilometers and let me just set this hot tea down. Okay, so here we go. Training for two marathons in two weeks. How am I doing it? So listen, I'm putting this 14 day window into the depository of perhaps down the road a coaching folder. Meaning I'm hoping this experience of going from Amsterdam and just so all the new folks out there, I'm running two road marathons in 14 days. You don't usually do this if you haven't seen the vlog from about four days ago as to why I'm doing this. Go check it out, upper right hand corner. I explain the entire situation. So Amsterdam Marathon was seven days ago. Amazing. It feels like a lot longer ago and then New York is exactly seven days from now or when you're watching this about six days.  Now as far as how my legs feel right now, I would put them at an 8.9 out of 10 on that scale and 10 being the soreness is completely gone. I still have seven days later a little bit of soreness left in the legs but I'm hoping by Monday tomorrow that the legs are 10 out of 10. That's my goal is that when I wake up the soreness is completely gone. But I know that doesn't mean my body is recovered. No way. I've heard based on you know books that I've read and just listening to interviews from people that race marathon have been racing marathons for years and years at a high level that it can take like six to eight weeks before your body fully feels recovered. Now you might feel better but where like everything is back to normal your joints your bones your neuromuscular system all of your anatomy. So right now even though the goal is to get all the soreness gone by tomorrow so I don't feel anything in the legs I know that my body is not back to normal. Now the Amsterdam marathon it was if you've been watching consistently and I know you are out there thank you it was the peak race like for the fall racing season. It was basically the pike's peak ascent and then another marathon whether it would have been Cleveland or then I had to pivot after the injury to Amsterdam. So because I did not hit the 219 so I went back and looked at the stats from the Polar Vantage V-Watch I must say pretty handy to go back and look at even though I think during the race I maybe looked at it a little too much or maybe not enough depending on what argument you use but now looking back it looks like around mile 18 I really was hitting the wall. I was going way too much into oxygen debt the lactate threshold was you know definitely building up in my legs or sorry the lactate was definitely building up in my legs and I started to fade pretty hard at mile 18 which means the last eight miles I was I was holding on for dear life especially like the last two miles it got pretty ugly but so because of that experience toward the end of the Amsterdam marathon how am I approaching New York City? Oh man okay bear with me here and I'm going to try and not get into race strategy for New York City that's going to be next week but I received through the Amsterdam experience I'm going to put it and you might argue in a different direction but I'm going to say I received a very very solid aerobic benefit from that let's just say that 18 miles of five minute to five oh eight pace maybe five minute to five ten pace like right in there I was able to hold that pace for 18 miles and I know it was too fast but at the end of the day that just doesn't go out the window because it's a race and not a workout I still I believe am going to benefit from that aerobic stimulus and frankly anaerobic stimulus in New York but the big caveat is the rest of the body because the body works together so it's the legs it's everything else the back it's it's everything and I know New York is a hilly course so I'm not going to get into race strategy but as far as how am I getting ready for New York and how in coming off of Amsterdam I'm approaching it mentally that who knows maybe I can still go out and put a good effort forth on the streets of New York okay that's that's my mental state at this point seven days to go and so now what am I doing exactly to get ready for two major road marathons in two weeks okay here we go you're ready for this I had to write it down because I'm doing I feel like quite a bit to take care of the body so this is more of on the recovery side and then I'll talk about the training side here in a minute okay here we go four massages in two weeks that's right and now listen true love and I we could not we could not afford four massages in two weeks one year ago I realize like people message me and they ask me like listen I have 80 bucks should I do a massage like before peak race or after a peak race usually I tell them to do it after but like I realize like it but you know if you're on a tight budget you just can't go out and get massages every week but just remember this is like patience consistency in everything in life and sure enough a year later I've saved and pinched pennies to the point where true love and I talked and we said hey Amsterdam was great but let's see what can happen in New York so therefore I know based on how my legs felt walking off of that airplane in Denver six days ago like I had to take care of the body as much as possible which means four massages in these two weeks in between the two marathons okay moving on uh getting into the gym 12 times that's right in 14 days I'm getting into the gym that's my commitment 12 times not necessary not for weight training a little bit a little bit of upper body work but more for the stretching the pool uh and the sauna okay and the foam roller even though I could foam roll here but uh the stretching the pool and the sauna all right uh hammering the elite recovery mix from UNIVED I must say I do feel better after I put a packet down every single day we talked about that a couple days ago so the UNIVED elite recovery mix is treating me well I just um I'm a fan right now I really am a fan all right moving on 10 I know this is getting into real detail but that's why I'm here is to communicate with all of you 10 and I okay this is what I do but 10 epsom salt baths at home hot water um if I had access to an ice machine you know at my house like a big ice machine I'd probably mix in a few ice baths as well especially uh closer to I guess closer to Amsterdam but right now I'm doing 10 epsom salt baths at home between Amsterdam and uh New York all right so foam rolling and stability disc for 15 to 20 minutes every single night okay 15 to 20 minutes foam rolling and stability stability disc here at the house and the foot log of course um sleeping seven to nine hours every single night uh I have missed a couple because of jet lag where I wake up at just odd hours uh but I'm doing I know you know we talked about this uh maybe three or four weeks ago sleep is our best friend when it comes to recovery okay we that vlog upper right hand corner as well and yes simply say no to tasks that require me to be on my feet and I know this is being a little I don't want to say selfish but I'm just prioritizing for example you know how much we love eating and having grilling out on the uh patio out in the carport on the other side of this wall well it's snowing now and so it's my responsibility as the husband to get the outside of the house buttoned up for the winter meaning wrap everything up in a tarp well I've pushed that to the to the wayside and true love approves that it just has to wait until after New York maybe even after Argentina just because I know like that's probably a two to three hour project and I don't I can't be on my legs on my feet for two to three hours straight lifting heavy furniture um and so it's it's just prioritizing rest and recovery okay and okay moving on to training I'm not gonna keep this concise today I did 13 miles felt good um still a little sore but overall a little more up tempo you know about 650 pace and uh tomorrow yes I'm going to do some k repeats just a little bit maybe four maybe six maybe eight not nothing crazy but if I'm going to go to New York I'm going to go to New York uh mentally prepared and how I feel that my legs will be ready to continue the turnover that I had in Amsterdam keeping in mind that I'm going to rein it in not chase that butter rein it in rein it in okay and lastly I'm going to do a little bit of hill work uh on Tuesday because of the bridges of New York City so nothing crazy but some some rolling hill work because I just want to make sure that I'm mentally in the groove to feel like I can approach the bridges in New York the 950 feet of elevation gain and loss in a good uh confidence state all right so that's basically my training everything before this uh leave the after Amsterdam was just recovery and survival frankly to get the legs back to where they are at this point and then from Wednesday Thursday Friday Saturday baby baby baby easy easy easy rest rest rest just chilling out and letting the legs just say okay I there's not much we ran out of time you know there's not much else we can do I just need to chill out and rest all right that's my strategy I know that was a lot between two major marathons in two weeks that's what I'm doing and again we'll see what happens at the end of the day you have to test you just have to test and again I hope to put it into the depository of coaching experience you know future coaching experience down the road being able to draw upon these two weeks if there's athletes out there that are like hey I was accepted to Berlin and New York City or Tokyo and Boston in the same year they're very close in time what should I do how do I how do I get through this uh this situation which is a good situation it's a great situation to be able to race Amsterdam and New York it's all good it's just it hurts and you have to be careful and you got to watch your and you got to you got to prioritize the I would say just take it listen into the body listen into the body and that's that I love you all thanks for being here all right we're going to sign off we're going to toss it back to these sleeping vlogs on the right that I talked about and then on the left the other vlog I mentioned in this in this little rendition here in the studio all right everyone seek beauty work hard and love each other oh we will see you tomorrow


Good afternoon. My name is Xiao Jingjiao. Today I will talk about how to leverage open-source information to make effective cyber attack and defense against different models. Before I start, I'd like to introduce who we are. We are Northwest Security, a team of independent security researchers and data engineers. Right now we have five members from left to right, Chen Li, Yang Chuan, Li Yang, Xiao Jing and Li Wei. We have diverse backgrounds but we do have a united interest which is to apply AI techniques to solve security issues. We have been fascinated by those ideas coming out of interaction between AI and security. So we have been actively looking for opportunities to learn more along that direction. And we also, we are the team got invited for the CED CTF, which is the first CTF competition on adverse service attacks and defenses. So as a new team attending the CTF for the first time, we learn a lot from beginning of preparation to the end of competition. So today this talk gonna meaning, sorry, gonna meaning folks sharing our learning from this event. So this presentation gonna only include two parts. In the first part I'm gonna talk about how do we evaluate open-source attacks and defenses. In the second part we're gonna introduce our design of the defense for the CTF. So because due to the limitation of the time and the resources, we didn't try to develop our own method for the attacks and defense. So basically our strategy is just try to utilize or leverage the public available information or open-source code. So in order to select those methods, we have to define criteria. So we define our criteria based on the requirement of the CTF. So there are three factors we are considering, speed, transferability and the strengths. For defense, because CTF has a hard requirement on how fast the image classifier has to run. So we put that one as a top one priority. So the requirement is two seconds per image. And also because we have to, we only can submit one defense. So transferability is also very important. So we even sacrifice our strengths and to to enhance our transferability. And on the other side for the attack, because there was no limitation on what kind of attack we can use, how many times we can attack. So basically we only consider the strengths of the methods. But actually this strategy turned out is not that hundred percent correct. So because we just finished our CTF, actually the speed for attack is also important because we implement some strong attacks, but it takes way long time. So we don't have the opportunity to try it. So basically every time we each run we only maybe just try maybe like one or two times. That's it. So next time maybe we can improve on that. So we use a target rate to evaluate the strengths of the method, which is a percentage of the images being misclassified as target classes. So keep those criteria in mind. We start our evaluation. So basically the evaluation starts from building a baseline basically. So we do a benchmark of the base defense attacks. So to do that we use two open source libraries for attack. We use clever hands, which is a Python library which targeted, which actually built to benchmark the machine learning system against adversarial attacks. So it's basically the collection of different adversarial attack methods. And for defense we actually Google provided those pre-trained weights actually. And they even provide those adversarial trained models. So what that means is those model is trained by both original image and also some other several images. So there are actually there are two popular architectures. One is an Inception V3 and another is an Inception ResNet V2. So in order to do the evaluation we have to use some test data set. So basically the test data set we are using is provided by CAD, which includes thousand fresh images classified into thousand classes with ImageNet labels. If you look at the right hand side that distribution target class versus true label, you can see the width of the distribution along along the X direction is very consistent. So that means those target class is uniformly selected from zero to thousand. So which means this is an average case of a scenario. So what that means is if we use this data set to evaluate our method, that's going to give us an average estimation of this method. It's not the best case and not the worst case, it's just average. So by using those two libraries we start our evaluation. So this table shows a target rate of four attacks against three defenses. So all four attacks use the exact same gradient based method which is a basic iterative method, DIM. The difference is the first three, the different model has been used to calculate the gradient. So basically the attack is calculated based on the gradient. But those three attacks use different models. Actually they are corresponding to those three defenses. So basically the model in 7v3 and also the adversarial training 7v3 and ensemble adversarial training in several residents of V2 has been used to calculate the gradient. So basically we can see for each column, for each defense, we are doing a white box attack. So we can see the white box attack is very effective actually. For example, for the first one, BIM based on inception model, we can have like 89% hit target rate. But even for the second one and the third one, the second row and the third row, those two, those two, sorry, the second column and third column, those two defenses, those two defenses have been adversarial trained. Even for that model, it's still vulnerable to the white box attack. We can still have a very high like 75% and 85% hit target rate. So but we can also see even though the white box attack is very strong, but the transferability is very bad. So basically it only works for that model. For example, if you look at the first row, it only works for in 7v3. But for another two defenses, the hit target rate is zero. So it's totally it doesn't work. But we can improve that by doing some assembling. So basically the last attack ensembles three different models together. So the basic idea is we calculate three different adversarial images based on three models. Then we just calculate the average and use that average image as our final attack. So by doing that, you can see we can keep, we can still keep a high hit target rate, but also improve the transferability. So basically it works for all three defense. So that's just because I forgot to mention, so because those two levels are publicly available. So basically we treat those when we analysis those performance, we just treat those like a lower boundary. So basically if we pick the attack or defense, it has to, it must be better than those methods. So now then we try, we also try some strong defense like Guided DeNoise, that's actually the author is sitting in the audience. And also the Random Padding. So both of them actually the first and the second place in the 2017 NIPS Adocera defense competition. So you can see those two defense are very strong. So basically for those four attacks, the hit target rate is just zero. So it's just totally not working. But for us, our strategy for attack selection is basically just make sure we have a corresponding attack for each single defense we can find. So because there's no limitation on how many times we can try. So basically we build a set of, a broad set of attacks for this CTF competition. So this diagram is a polar plot, which is a polar plot of a hit target rate. So basically different colors stand for different attacks and six different defense is located on six different angular position. So from this, this is like a needle-like branch. So this needle-like branch pattern actually can tell us two kinds of info. One is the length of the branch, which is the value of the hit target rate. So which actually is the strength of the attack. And the number of branch, which actually tells us how good the transferability of this method is. Because the more branch we have, which means this single method can attack more defenses. So basically through all our evaluation, we use this plot to evaluate all the methods. Just trying to get a balance between the transferability and the defense. So those three methods we implemented. So for guided denoids, we kind of, we kind of like, because they are, they open source of everything. So kind of, we just implement a wide-boss attack. So actually it's very effective. We can get like 98% hit target rate. And for another two kinds of defense, like a random padding, basically they randomly adding some paddings onto the input image because they try to utilize the transfer environment feature of the CN-based image classifier. So basically for our original image, if you add random stuff on it, it will affect the classification results. But if we input a fake image or an adversarial image, when you add some random stuff onto it, it's going to strongly degrade the influence of that attack. So that's the idea of the random padding. So we also implement some, adopt some method to implement target attack for that. So now I will talk about some strategies we applied in both defense attack. So basically for the first row for defense, it's called, it's very popular one. It's called ensemble adversarial training. So basically we not only just train the model with both the original image and adversarial image, we also try to generate those adversarial image from several different methods. So we try, by doing that, by doing this ensemble adversarial training, you can strongly, you can effectively improve the transferability of your defense. But on the other hand for the attack, you can also apply ensemble adversarial attacking, which also can improve the transferability of your attack method. So basically same idea you can apply for both defense attack. Another idea is for defense is a gradient masking. So basically right now for those popular attacks, most of the popular attacks is a gradient base.  Then based on the gradient you can, you can generate your attacks. But this gradient mask is just trying to hide those gradient info. So you, they can, they can just make gradient unsmooth. So which means it's kind of like stepwise. Or you can, they can just make the gradient vanish totally become very small or very large. It's exploding. So basically when you get those info it's just guide your calculation to a wrong direction. Basically by doing that they can achieve the defense in purpose. But still on the attack side, researchers still also propose some corresponding attack, attacking strategies. Basically if you make a step, stepwise the gradient, basically you generate a lot of local minimum. So then when the attack try to escape from those local minimum they just, they can, there's one way they can do is they can just add a random prohibition each time onto that calculation. So basically when you start into some local spot and you just randomly jump, jump it out. That's just this idea. That's the idea of a random prohibition. And also some, some attack they use a gradient smoothing. So basically when you calculate that, when they get a gradient info they apply a Gaussian smoothing and smooth that gradient out. So everything is perfect for the attack again. A third new method is called a backward pass differentiable approximation. So basically it's approximate. So basically this method is just, we cannot get the real gradient from that network. Then we can, then somehow we can approximately calculate one. So basically that's the idea. So we don't, we don't use the target to calculate a gradient. We just somehow come out of an idea, get a close one, then use that one as our gradient, then achieve the, the attack. And the third strategy for defense. So basically the gradient is an adversarial attack, which is a, is because of the adversarial, adversarial noise. So it, it is a noise. So very, very natural solution for the noise is just do a filtering, just get rid of noise. So that's the third, the, the basic idea of the third one, which is, which is use some image processing method. Just, just filter those noise out. But still from a tag side, people still figure out how to attack this kind of defense. So basically what you are doing in the defense side, I just doing the same thing on the attack side. When I calculate the attack, we just improve those filtering or anything inside it. So when, when I calculate the attack, it's going to take into account everything. So that's the idea. So actually we use this, we use this method to achieve the attack against the random padding. So basically we, when they calculate the attacking, our third image, we also add the random padding into a iterative calculation. Then the last one, I think that's the only one, there's no effect attack method, so which is a detection only method. So basically it's not enhanced the, the robustness of the system. It's just add on another module to attack. If there is a adversarial attack happening or not, if it's happening, we just give an alert or we just, just returns some random stuff. So that, that's the idea of a detection. So that's all about the evaluation. So based on the, so we do a, we do a very extensive evaluation of a lot of attacks and defenses. Then based on those learning, we came out the, this design of our defense. So basically this defense include two modules. The first one is the left-hand side, that big, big rectangle, which actually is our image classified. But this image classified include two image classified. So basically we paralleled a line, those two image classified together. Then we put a difference filter at the end. So basically what is happening is when there is an adversarial image coming, it's going to get classified by both of those two classified. One is a CV2 plus random padding. Another is a guided denoise. So then there's, there are two labels output from those two classified and part is going to get fit into that difference filter. So basically we're going to compare. If those two agree with each other, then we think that's the attack field. Basically we, we get the true label. But if they're like, we don't agree with each other or contradict with each other, then we just simply return a zero random number. So that's the idea of the first module.  So, so basically CV2 is the name of the open CV package in Python. So basically it's, this is also an image processing idea. Because when we are doing, we read a lot of codes, they are doing a lot of, they're doing the image processing through a lot of different ways. But then we think why, we just, why don't we just use the professional packages just doing the image processing. So open, open CV is the very famous open source library. They are just doing the image processing. So they provide a lot of sophisticated functions. So basically you, and also it's, it's implemented by, by C. So it's very fast and very easy to use. Basically you can just use the function, just add a one line. You just, as simple as one line code. Just add that filter in any existing defenses. So right now what we are using is called bilaterary filtering. So which is edge preserving noise reducing smoothing filter. So basically what it's doing is this, actually those four images show the effect. So the left, left up corner, the first one is the original adversarial image. Then if you, if you go through those filter three times at the last one, the bottom right, that one, you can see that on those funny, funny pattern on the background, those actually is the adversarial partition, try to, try to confuse the classifier. So basically by going through all those filters, those noise is smoothed out, but at the same time the edge is still preserved. Because we know for those image classifier, they kind of like they, they try to learn those edges. So if you can maintain those edges, which means this matter won't affect your classification accuracy. So to approve that, we apply this CV2 filter onto the guided denoise defense. And use it against three different attacks. So this time those color patterns stand for defense. And the left up corner, that one is what we implement, what about attacks against this guided denoise method. So you can see the yellow one, which is the one we use CV2 into the, this guided denoise method. It can effectively reduce the paytackly rate from 96% down to like 45%. So our method, our method is, this CV2 filter is actually an effective method. And also the reason why we put another two defenses, another two attacks on the, on the, on the other side, is just try to see if this CV2 filter gonna degrade your transferability. But it's turned out is not, it's not, it doesn't do that. So we think, we believe this CV2 filter is a very effective way to at least enhance your defense. Because it's very fast and it's also very flexible. You can, you can apply it to any existing system. So that's about the first module. So the first module, the successful defense, defense using the first module, relying on an assumption, which is there's no attack which is strong enough to fool or confuse both of them. But that's not true actually. Even, even we know that's not true because we do a, when we do the testing, there's some attacks which can fool both of them. CV2 plus running pending and also the guiding hours. So basically both of them gonna output the, the exactly same label but that label is wrong. So which means the first module gonna fail if, if that happens. So that's, that's where the second module come, come into play, which is a Keeming filter. So this Keeming filter is based on a machine learning method called Keeming clustering. So the assumption here is this Keeming clustering algorithm is robust against a neural network based adversarial attack. So in order to verify that we conduct a very little, a little experiment. So this is an experiment. So basically what I'm doing is there's a thousand original images. Then we generate another thousand adversarial images. Then they, then we put both those two thousand images into this Keeming, Keeming clustering. So this Keeming clustering are gonna, gonna put, put, gonna automatically classify those images into different classes. Then we compare each orange image's class against that adversarial version. Then we found that that actually a 94% of them are matched. So which means those adversarial attack don't affect the accuracy of the Keeming clustering. So we can trust this method. Then based on that method, I'm gonna talk about how we implement this Keeming filter. So basically the first one, the top one is, is describe how we generate a mapping because this, all the, all these competitions are based on the ImageNet dataset. So ImageNet has like thousand labels. But when we do the Keeming clustering, it's impossible for us to do like thousand, thousand, thousand clusters. So basically our Keeming is trained to classify the image into ten, ten classes. So how can you map a thousand, thousand, thousand different labels into a ten different classes? So that's, that's how we, that's how, how we do it. Basically we, after we train our Keeming clustering cluster, we use another test dataset. So that test set is gonna go inside into the Keeming clustering. We know those true label, those ImageNet label of those test class set. Then after we classify those image with Keeming cluster, we also know the Keeming classes. So that's gonna generate a map, a map, just basically the table. So we know which, which class gonna correspond to which ImageNet labels. So after that mapping label is ready, we start to finalize our logic. So basically it's very simple. So when an adversary image coming, first it's gonna go through the first module I just discussed. Then it's gonna generate a label by which, which is an ImageNet label. Then that ImageNet label gonna convert to a Keeming label by, by using that mapping table we just generate. So which, it's basically just a 10 elements list because we, the Keeming, we only have 10 Keeming classes. So this, those 10 elements actually is all, is all of the by probability. So, so the, for example, the first list, which means we think this label is most likely in class nine, but least likely in class four. Then, at the same time, this attack image also gonna rarely fit into the, the Keeming cluster. So Keeming class is gonna generate another list. So it's also a 10 elements list. But for this list, the Keeming thing, the Keeming things, most likely this image is like, is in, is in class two, but least likely in class nine. So these two lists actually they, they contradict to each other. They, they don't agree. So that's when we think, oh this filter thing, this attack actually succeed because they are contradict. So then they gonna just output a zero or random number. Only, only when those two lists match or some, or pass some checking, checking logic we said, it's gonna let the, the original label come through. So that's the idea of the Keeming filter. So that's all about the design of our final submission for the defense. So this is a, just a quick, just a same polar plot showing the hit target rate of our defense against some strong attacks. So you can see for all six attacks, the hit target rate is close to zero. So we believe our defense is effective and also has, has a good transferability. But it's turned out, it's not. We still lost some, some points. So I think, I think for the Keeming field, Keeming module, there's still a lot to improve space. So that might be a direction we, we try to improve. So in summary, I talk about how we select a set of attacks, a gaming, aiming different defense. And also I talk about the defense comprised of image processing, classified difference filter and a Keeming filter. With that, I'm gonna finish my talk. Any questions? Yes. You mean, is that a random or not? Yes. Yes. So we are, we are, yes. That's the, that's the main, main way to achieve the attack. You mean the attack method? Yes. Actually, so, so the attack actually is a, mathematically is, is actually just solving an optimization problem. So it totally depends on how do you, how do you define your problem? So there are some attacks, they can just only adjust one pixel. It's still, it still achieves their purpose to change the classification. And also they have some, some research proposal, some, I don't know, sort of patch. So basically it's just a pattern, a patch. When you pull that patch onto anything, the image classifier gonna misclassify that image.  So from an image perspective, how the attacks and defenses work. Yes. How can you translate those same concepts to, let's say, a piece of malware that you're trying to process through AI or ML to classify whether it's benign or malicious. How, how would we say, okay, this is, this is how we modify the piece of malware with noise or adding new processes through the AI. Is it literally the same way that you would modify the image to be able to tamper with the algorithm? Okay. So your question is just asking if the same, same method can be applied to a different other AI applications besides. Is it literally the same way or is it completely different at all? I think fundamentally they are same. So it's, it's still another kind of optimization problem. You just try to solve that optimization problem. But in practical, when you're doing attack against some other stuff, even though it's not that far away, like I'm doing the same attack against an image classifier, but in, in real, in the real world, there are more, there are a lot of factors has to, has to be taken into account. So, for example, there are some, some research about the, so right now what I'm, what I'm talking about is totally in the digital domain. So basically everything achieved in the computer. But if you want to achieve a physical domain, like you just put something on the wall on the, like on the washing machine, the washing machine is going to miscalify as a dryer or something. For that, you have to take into account, like the light, the, the, the wheel angle, all those factors has to be taken into place. So basically it makes the problem more complicated, but I think fundamentally they're same. Yeah. Yeah, actually that's a, that's a very good point. So yeah, that's why we, so there are a lot of different ways to apply the same idea. So basically it's, basically what, what we are trying to do is we try to get rid of the, the bad noise, which is adversarial noise, but still keep the value information for the classifier, because you cannot just totally get rid of everything. So that's going to also going to mess up your classifier. Because I'm actually even for this series two filter, I didn't show here, but if you're, if the parameter didn't set correctly, the accuracy of the classifier going to strongly degrade. Yeah. So basically, yeah, that actually that's a good point for, we can try to do something in the frequency domain. Actually that's one of our team, yeah, team member, yes, yes, yes. Actually that's, that's one of our team members. He's trying to work on that direction. Basically it's, it's not doing the image filtering. We're, we're trying to look at the frequency domain to tell if it, if it's a real image or not. So that's, that's, that's our idea. Yeah, yeah, that's another way. Yeah. And also because what, even though you carefully set up your filtering algorithm, it's still going to affect the classifier accuracy. So basically I, I read some paper, basically the researcher is doing is this, they use another set of a neural network, just try to calculate something called class activation region. So basically when the classifier look at the image, there was an interest region or those not interest region. So basically that neural network just try to figure out which region is more, more important, gonna affect the final classification for that classifier. Then when we know that region, when they apply the image filter for the attack, we don't touch that region. We just filter everything else out. Yeah, that's, that's the, yeah. Yes, yes, yeah, yeah, yeah. But that's gonna be slow. Yeah, yeah, yeah. Okay. I have one more. Oh. Nobody's gonna ask, I have one more. Okay. So you, you, you talked about processing the different images through different filters to increase the accuracy, right? Yes. One of the things that you mentioned was carrying out of the serial versus originally images to kind of, you know, have a, have a baseline that you can use to, you know, understand, right? Yeah. So does, does the accuracy improve the more images you feed into, into your dataset? Or is there a certain point that it doesn't matter how many images you throw at your, at your dataset that the accuracy just peaks? So I think that depends. So it depends what kind of algorithm you are using. So for example, if you are, so when, if, if you, when you train that classifier, if you're taking into account both like pure original image or some image after filtering. So when you train that model, you're already taking into account that factor. So then when you do the image filter later, it won't affect that classifier. But if you, if you try to, when you feed more image, I don't know, I'm not sure I fully understand it, but you mean, if you feed a lot of image, I think you're talking about training, right? Yeah. So for training, yeah, we can, so that's actually, that's the one, that's the, the key point of the, the adversarial training. So basically, the adversarial training is mixed of the original image and adversarial image, but we can also do a mist of original image and some image after image filter, right? So by doing that, we treat, we train that classifier, that classifier gonna learn, okay. So some image is original image, but some image is after some filtering, but I can, I can still identify their same. So that's kind of how you, how you build your training, training dataset. But let's say you had unlimited time, the accuracy continue to improve the more you, you feed, or is there a certain point where it just peaks, no matter how much more time you train? I think it's that's, that's limited by the, the neural network is not limited by the image filtering. So basically, I think it's gonna reach a peak. I don't think you can just keep going. Yeah, I don't, I don't think, I don't, I don't feel that. Yeah. Okay. Okay. Thanks.


to help. I'm Jay Fidel. This is ThinkTech. The handsome young man is Peter Hoppenberg. He's a professor of history at UH Manoa. We're talking about how history can help us appreciate the genocide and compare it and contrast it to the Holocaust. And that's especially appropriate this time of year. Hi, Peter. Good afternoon. How are you? I'm good. So Peter, I guess over the past few weeks when we see Vladimir Putin killing people in large numbers and using weapons that are dedicated to kill civilians, not military, when we see and hear about the atrocities, it really does sound like a determined effort to kill people. Weapons of mass destruction effectively killing masses of people. And it's inevitable to think about where else in history this has happened. I guess it's happened in Syria and Chechnya. It's happened in many places in Africa, to some extent in Latin America. And I suppose you could look through American history and find examples of the same thing where one given state or power was dedicated to killing as many people as possible. Think of Bosnia. And they may have lived together for centuries. And one of the questions is this written into the DNA? Or is this something that you can somehow electromagnetically get people to go out and kill or observe or participate in large killings? And then of course, the Holocaust, which you and I were raised to understand was the worst possible evil known to man, where we said never again, may not be prevented again. And so I think it's worth, it's a valid discussion, an important discussion to compare what's happening now and what Putin has done on other occasions with the Holocaust and find comparison points and contrast points. Have you been thinking about this? Well, you know me very well. I think about it 24-7, as my children will tell you, my bookshelf is filled with it. And I think for viewers as well, not only your excellent perceptive point about Russian Ukraine, but this is also a time where we remember the Armenian genocide, the genocide during the First World War, but also plan before the war and continuing after the war. I mean, we focus on what the Turkish forces did during the war. And this is a time in which, after a long political struggle, that is recognized as a genocide. And next Wednesday is officially Yom Ha'Shoa Day, where the sirens will waft in Israel, and folks will stop for two minutes.  So this is a particularly appropriate time. And as you say, most appropriate in people's homes, because we can talk about whether what Mr. Putin is doing is genocide or not. And I think it's an important discussion. But it is certainly a war crime, and it certainly crimes against humanity. So among our discussions, they might be trying to define genocide, not in a way to limit other horrors, because you're right, there always seems to be time for killing. But genocide has a specific international legal definition. And whereas folks could justifiably, it should be upset about what they see. Reaching that decision takes research, and once that decision is made, then certain political actions actually are required. I want to look at it from the point of view of a naive person. You know, a naive person at the Passover table, for example.  You know, what's the difference? Is there some kind of demarcation between, say, 50,000 people and 6 million people? That shouldn't be a difference. You should be able to do genocide with any number of people, right? No, you're absolutely right. And that's where, when we consider whether something is a genocide, it's not the numbers. It involves intent, and look, intent can be 50,000 or 6 million. It involves intent, it involves specific targeting. So for example, what would be a genocide in Ukraine is the intentional targeting for murder, rape, separation of children from parents. Those are all considered genocidal acts, specifically because the victims are Ukrainian. So not because they live near a city, for example, or not because they're in the way of troops. But genocide requires the identification of a people, and then the intentional elimination of that people, specifically because they are that people. So it could be, look, all numbers are horrific, right? But it could be a relatively focused lethal episode, or it could be, as in World War II, across all of Europe. And the numbers don't really matter. What matters is being able to prove the intent and to prove the consequence. Why don't you read the language out of the convention? I will. I'm sorry for the audience if I have to look down a little bit. This convention was articulated by the United Nations. It was very controversial, remains controversial. I'll be happy to talk about some of the controversy. But let me just point to three articles, the first three that will help our viewers. Please remember the dates, because the dates are important. This was discussed and approved in 1948 and became international law in 1951. So when Jay says never again, there have been far too many incidents of this since 1951. Okay. Article one, and let me read slowly. I think the word is important. The contracting parties confirmed, so the contracting parties and the members of the United Nations that approved this, that genocide, whether committed in time of peace or in time of war, is a crime under international law, which they undertake to prevent and to punish. And I don't want to bore the audience with word smithing, but almost every word is important. When the term genocide was initially created as an artificial word, the understanding was that it occurred during wartime. So this is an important revision. The word was coined in 1942 and 90 years later. Very importantly, and part of the reason that it was added is there were genocides during decolonization. So for example, in the British left, India, there were genocides between and among Hindus and Muslims, but that was not technically a war. It was technically peace time, right? It was transference of power. Okay. A crime. Well, that's a bold step, and that requires you as a former lawyer. No, right? It requires statutes, requires evidence. One is innocent to proving guilty. So as much as we hate somebody and as much as we might see things on TV, there's an international court which decides. And then under international law, and that's been very controversial, for example, the United States and the Soviet Union are not signatories on a lot of these international laws. Okay. Undertaken to prevent. Well, that's a big step. We're going to try to study this. And as is debated right now, trying to, while we see something on TV, trying to prevent it from getting worse. This law requires governments to do that, not just to punish. I think most of the viewers will recognize the punishment, right? The Nuremberg trials, Milosevic from Serbia tried several of Sudanese officials tried, but that's exposed factor, right? The bodies are already dead. So it's both to prevent and to punish. So let me stop there and see if you have any questions before we go to article two. Well, I think the most, the most profound part of what you read is that the United Nations, all the members of the United Nations, I guess that was unanimously passed, said that they would, that the United Nations would prevent it in peace or war. And since then, aside from the never again statements, there have been many genocides and the United Nations has never been effective at stopping any of them. And two thoughts come to my mind about that. One is international law, as you know, is weak, hard to enforce. You know, in law school, they told us international law is kind of ephemeral. And it comes, it goes, it depends on geopolitics, sets of time, and so forth. So it's not a reliable container for a law that requires the organization to take action either by stopping a genocide or punishing a genocide.  And if we catch you, we're going to do something to you. And they have that in Europe now, something called universal jurisdiction, where they tried a Syrian general a couple months ago and that worked. And we don't have that regrettably. The other thing is that it's a built-in flaw. You can't refer to the International Court of Criminal Justice unless you have a referral by the Security Council. And the Security Council has some bad actors on it. They're never going to refer claims against themselves. So you therefore neutralize the United Nations in the case of Russia and China. Members of the Security Council who are capable of vetoing any referral. So, I mean, I don't think it has worked. There was a time when the blue helmets went into Rwanda. And then a few days later, there was a time when the blue helmets left Rwanda, leaving it to a full-scale genocide. The other point I want to make before you go on is this. I've been wrestling and we have had many discussions on think-tack about this as to whether the human tradition includes genocide. We just get together and kill one another in large numbers every so often. And historically, as a history professor, somebody who connects the dots on this sort of thing, I'm sure that's a tantalizing theory for you. But the other theory is that it takes someone who is evil and competent and clever, like Putin, to initiate genocide. It doesn't come up as a matter of a community emergence of a need for genocide. It comes up because somebody triggers it. Somebody always triggers it with propaganda that reaches the people who would conduct the genocide. And that's clear in the case of Putin. He's doing it. He's leading it. And if you go back and look at some of the other genocides that you and I have mentioned, I think we can find that in each case, there is a person using the technology of the time to, at the scapegoatism, if you will, at the time, for political purposes to engender a genocide. So it isn't part of the natural human collective conduct. Rather, it's somebody fomenting that conduct. It always requires an activator, a trigger. Okay, those are my thoughts on what you've read so far. All right, thanks. Let me continue because we're on the clock. We can get back to some of those points as well as far as leadership or not. But let me just articulate these for our viewers. Article two, I think will be helpful because it tells you what actions are considered genocidal. And your audience will know that this has changed a bit where we've added questions of cultural genocide now, for example, forbidding a language or forbidding a traditional practice. In 51, it was pretty explicit about explicit acts, killing members of the group. That should be obvious. But again, because they're members of the group. So if I'm Russian, I kill the Ukrainian, not because they happen to be in a strategic house. I kill them because they're Ukrainian. So if the viewers have been following along, some of the evidence we've been seeing, and again, it all needs to be verified, are, for example, Russian women encouraging their men. And this is a legend. They're meant to rape Ukrainians because you're Ukrainian, right?  Secondly, and I think for 1950, 4851, this is pretty perceptive, causing serious bodily or mental harm to members of the group. So I think that UN was thinking I'm the bodily one, it's obvious, but the mental harm, the use of propaganda, the physical isolation, imprisonment, I mean, imprisonment may not be bodily harm, but it's mental harm. So essentially what they're saying is these people as members of this group are not just being punished, they're being excluded from society, they're being isolated. Can I respond to some of that? Please do. Okay, I think those less murderous events within the subsequent expansion of the definition, dilute the definition. It's very interesting, but it's very hard to prove. And it doesn't rise to the same level of outrage, of crimes against society, of shocking to the conscience as murder does. So when you start throwing in all those other things, it's a kitchen sink and it dilutes the primary one, which is mass murder. That's what we're talking about. The other thing is a group. I think we've seen the world change since the Holocaust. That was certainly a group and that was what it was about, right? In the late 40s, what else would you be talking about? It's a primary object there. And in fact, I think what happens in Ukraine, what is happening right now today in Ukraine, is a strange mutation of that because the group is everyone who's Ukrainian. And how do you know? Well, it used to be 44 million people and I got 40 million people in the country. They're all Ukrainian.  They're all Ukrainian.  It almost sounds like you need a new definition. Unless one were to think of expanding it in somewhat of an existentialist way to say it is among the groups are peoplehood and that's been discussed.  Now, as far as the cultural editions like language, for example, or traditions, there are many folks who agree with you that those are a different type of crime and not a crime of genocide. But I think whether we, you know, re- or disagree, and it may dilute whether we disagree, that's on the agenda now, most certainly. Who's agenda, Peter? Well, I think many people's agenda. What I'm saying is that there's no particular organization looking into changing the rules that you're reading. Well, I don't think that changing the rules have has been a change of definition. But that's a change of definition, which I don't think we should be surprised at.  Those also who oppose the change commit that kind of genocide. I mean, it's to the advantage of people who forbid a language to say, well, that's not really genocide. So in the, in the early discussions, you mentioned, you know, the politics of all this. Well, the politics are on the right and on the left about this. So if we close on the right, I think the left is too loose with it. And people on the left think that the right is tight with it, because by being that tight and limiting to specific acts, they're less inclined to be charged with genocide, right? So the argument works both ways politically. But if you were to argue that a group or a people are targeted, and that group or people and the source of their identity is being demolished and destroyed along with them, or even perhaps without them bodily. So for example, when Australia took for years and years and years, took Aboriginal children, took them from their homes, and basically attempted to make them white. So they go to white homes, the white families would marry them if possible to somebody white, and essentially remove their aboriginal identity. Now, yes, compared to a lot of places, Australians probably Tasmania, Tasmania is a case of genocide certainly. But you know, you would have to argue that that is a form of genocide in the sense that you are attacking and removing a people specifically because there are people. Now the thing about the Holocaust was, you know, all Jews, religious, not religious, right? Maybe people who looked Jewish. Okay, but that's on a larger scale to what was happening in Australia. So I think we can discuss more about this. But I think part of the change is the sense that today, for a whole variety of reasons, what we consider identity is wider, more diverse than in 1951, where identity seemed to be on the UN scale, religion, ethnicity, nationalism. And those are obviously very strong foundations. But I think today we could talk about other forms of identity and, you know, whether people like those forms of identity or not, that is part of humanity. And the laws should protect. Getting back to that is the idea that this law is fundamentally supposed to protect minorities in the state. Not surprisingly, right? Usually it's a majority destroying a minority. So if you take that as a principle, and that's not a principle of democracy, that's a principle of liberalism, that you need to protect minorities, then you need to respect how that minority identifies itself. Particularly, particularly right, if that identity is not a clear and present danger to the majority.  Well, that goes back to my point about the United Nations at the inception, suggesting, encouraging, requiring member states to adopt laws within that state.  And I think one of the problems about all this definitional discussion for all these years, 70 years of discussion about what it really is, the United Nations, which adopted these rules of international law or encouraged the adoption, hasn't been able to stop any genocides. They happen here, they happen there.  It's mass murder. And the United Nations has no effective way to stopping it. And if we look for reform, the reform is not necessarily in reforming the description or the definition. It's in making an organization that will have the power and physical power, steel on steel power to stop this. And the United Nations is not that organization. The reason we had the trials at Nuremberg was because the United States won that war. They subdued the Nazis. They could do what they wanted in Germany at that point. We're not in a position to do that in Russia. Or for that matter, if you take a look at the Security Council, any place. So the real problem is you need, I'm sorry to say, you need a war to stop. You need steel on steel to stop a genocide. And we don't have that. And we need to develop a new mechanism to have that and to stop the genocides. Otherwise, as now, as right now these days, I mean, really, sanctions. You know, ideally, you would have been American troops going in there and saying, no, we're the policemen at the elbow. You cannot do this in a liberal world order. It's not permitted under the rules of the United Nations. But we never got close to that miles away. We have to find a way to get close to that. Okay. That's probably another discussion, which I'd be quite happy to have with you. Let me just finish the article. I'm distinguishing the genocide in World War II, the Holocaust, with the genocides we have seen since. Right. And you're also separating the United Nations' role to prevent, rather than the United Nations' role to respond. I mean, that's an important discussion. Thank you. That's true. Okay. Because it gets back to, we can't do this today. It takes a year. But it's very, obviously absolutely essential why it happens. We haven't really gotten to that discussion. Right. And that's a full discussion is why and how. And you suggested the importance of a leader, but the leader who strikes the match has to have tenure. And so I'm happy to have that discussion with you maybe next time. Let me just finish for our viewers that the articles, because we're almost out of time. Part C of article says deliberately inflicting on the group. Right. So part of genocide has to be intense. And that has been for historians a debate which won't surprise people between intense and effect. Right. So the debate, for example, among the Spanish conquest, the Americas, the effect was genocide. The effect in Australia was genocide. You could argue the effects on the White Islands to great degree because of demographic crisis. But their historians want to contribute between intense and effect.  So as a lawyer, I offer you circumstantial evidence. You know, if I kill 50,000 people by sending rockets into civilian populations, it's not a mistake. It's not an accident. The OG whiz that I just killed 50,000 people by sending thousands of rockets into the civilian population, you could make a pretty good case on some circumstantial evidence that I intended to do that. Right. But if you actually have what we're trying to find, which are the emails or the orders, remember the great debate is, and it still is a debate among not serious historians, there's no text from Adolf Hitler saying kill Jews. Circumstantial evidence. I mean, seriously, that's David Irving and serious historians don't take that. But in other words, we have other things, right? We have orders from Hydra, right? We have orders from Himmler. That's that's the evidence. Okay. And let me just remind you that Nuremberg was not intended to be necessarily a war crime trial. It was it was to try or the Germans for committing atrocities, but it wasn't really a Holocaust trial. If you look at it, and so that's part of the issue, right? I mean, no people in America would not have sent troops to Europe to say Jews in the 40s. No, they wouldn't have even bombed Auschwitz to break up. Deliberately inflicting on the group, conditions of life calculated to bring about its physical destruction in whole or in part. This, you can see is in good part focused on political famine, right? If I don't allow food to get there, if I take food from there, that's deliberately inflicting. It's not deliberately, it's not immediately putting them to people's heads, but it's leading to their death. Imposing measures intended to prevent births within the group. Now, anti-abortion advocates should not jump up and down. This is not, this is not about abortion. This is about sterilization, right? This is systematic sterilization or the systematic rape of women, particularly in cultures where a rape will be considered, right? An act of impurity and the woman might very well be killed and certainly the child would not be accepted in society. So during the Balkan crisis, this was a not unusual exercise in the part of serves. And you see it today, right? Intentionally raping, okay? Which is not only violence against women, which of course it is, but it's also a way of demographically eliminating a people because the child born out of this rape will not be accepted in society. And lastly, and in 51, forcibly transferring children to the group to another group. So that recalls, for example, American policy of taking Native American children and moving them outside the Native American community, or the example of Australia. Finally, because I know we're near the end, Article 3, what acts shall be punishable? Now here again, as a lawyer and those who listen are interested, I think you'll appreciate genocide, obviously, but conspiracy to commit genocide. So there we get to the sense that it is your leader, right? But it's also the minions around the leader. It is the professors I apologize who got on the radio in Rwanda, and used the radio to call for the murder of other people, their conspirators. Direct and public incitement to commit genocide. So I don't want to be held responsible for this, but for example, we'd have to ask about the Russian Orthodox Church's leadership in this, right? What are they telling their parishioners about Ukrainians? We know the Church is supporting Putin. What are they actually saying to their parishioners? Attempt to commit genocide. All right? You don't actually do it, but you try to do it. That's enough. And so that might be the case in Ukraine and Russia. It seems to be an attempt to commit genocide, complicity in genocide. So you stay neutral, right? Or if you arm in one way or another, or you allow in one way or another the genocidal state to export its oil to your country, right? And they're committing genocide, you're complicit in the genocide. So let me stop there because I know it's 1230. Any questions about those articles? And those are just more specific ones. And then not surprisingly, it goes into what you were talking about, the mechanisms of how the UN will go about determining it, what will be done specifically to the countries, etc. But I think for the viewers, the definitions are important because we have three, we really have four terms which we use, all of which are horrific. But somehow we've created a hierarchy. They are all horrific. A massacre is horrific, right? What defines a massacre? But a massacre is horrific, okay? War crimes, horrific. Primes against humanity, horrific. And genocide. So one of the issues is what is the compulsion to define something as a genocide when those three are horrible enough, right? Those are horrific. And that again, we probably don't have time to get into, but part of that includes politics and activating these laws, right? There's no law, there's no law which says that there's a massacre you have to respond. Maybe there should be. But there is a law that says if they're genocide. So the viewers will remember in the Balkans, right? The Clinton administration waited a long time to use the word genocide because that would have required some action. A couple of reactions, Peter. Number one, I go back to the notion of whatever that document says, it's only as good as the United Nations and the United Nations is impotent. It hasn't really stopped any genocides. It hasn't punished anybody. It has been useless. And I think the United Nations is over because there has to be a principal function of protecting the liberal world order or protecting the security of states and people and human rights. They haven't done it. Sorry, they haven't done it. So I think we need a new deal on the United Nations. The other thing is I really wonder whether these statements of crimes actually deter people from doing it. For example, let's say we have a tyrant, a despot, an autocrat. And he says, hmm, it's Tuesday morning.  And his Secretary of State says to him, no, no, you can't do that. That is against the resolution that Peter Haufenberg read the law of the international law the United Nations adopted in the late 40s. Do you think that ever stopped anybody from doing any of these genocides? I mean, it's possible that they said, oh, no, you can't do that. You can't do genocide. And the guy said, oh my God, I almost violated the United Nations law. How silly of me. Why did I think of that? Because they watched, they watched things, so they should have known it. Increasingly, they don't care. Increasingly, paper on which these definitions are written, the punishments are set out, are meaningless. And I think we have a world, see if you agree with me, where the amount, the quantum of genocides, of criminal, of war crimes, of atrocities, however you define them is increasing. And there's no policeman at the elbow to say, no, you can't do that. And if you do that, there are immediate and, and, and clenary sanctions on you. And I don't know what those would be, but more than what, what the coalition is doing now, because we will not permit you to do that for one day longer. That's what we need. And I think about Nuremberg, and I said before, is that it was, you know, United States had complete control. They could do what they wanted, and they chose to do that. And I guess it's satisfied a lot of people politically and ethnically. But I remind you that in the Nuremberg trials, there were 22 defendants, 19 were found guilty and 12 were sentenced to death. It's not a lot when you consider how many people died in the Holocaust. So even then, you know, there was more that could have or should have been done to deal with a country that had been largely complicit. We cannot afford to have this go on. We should have done more after the Holocaust. And I think we can do more now. So I don't think anybody who has any heart and soul would disagree that something substantially needs to be done in response. I think we need to talk about what the UN can do and has done. And I wouldn't be completely dismissive. I think statistically, you know, it's the old problem with this much information. We now know about most of the massacres that are occurring. The 20s and 30s, for example, when there's not lots of international information, horrific decade between the wars. So I entirely agree with you. Something has to be done. I entirely agree with you that there's too much of it. As a historian, I'm just going to take a little bit of the fifth as to whether things are worse or not, because they've been bad for most of the 20th century, if not previously. I think, again, we should come back and have a discussion about prevention. It seems to me that that's where the focus needs to be. I think stopping a genocide from occurring, if possible, I think that the Biden administration and others have done non-military endeavors, which are, look, I think if you respond in the way of army on army, you are creating additional carnage and additional war. I think the problem today is that, you know, India and China, et cetera, are not on board. If you could truly isolate Putin, truly, then you might be able to stop this. I think it was, in a way, unfair of the West to expect the Ukrainians to militarily stop this. There's been a lot of really hyperbole about no great success, this and that. You're not going to stop the Russian war machine by taking out some tanks for killing some generals. This is an in-balance war. So what do we do? I think we need to talk very much about prevention. And there is a think tank of nerds, well-intentioned nerds in the heart of Europe that is creating a historically-based, with additional contemporary data algorithm we wouldn't have called it that, but some of the viewers might be younger, to try as a social scientist to predict. That's still our best hope. Once these get inflamed, you are right, probably only war will respond. And once a dictator gets people stirred up, it's going to be difficult to get any of those people, anybody within a country to dissent will be arrested. So my sense is that we need to look at the causes. And the causes are varied. I don't want to take too much time. The causes begin really fundamentally with not having an open society that protects minorities. That's really where it begins. You can't ask everybody to fully assimilate and everybody to like each other. That's not going to happen. But you can create a society in which, without purging minorities, without ethnic cleansing, say Rwanda are a good example. Their minority in Burma, most of the evidence suggests there is a genocide occurring. But there are no legal protections for Rwanda and others in Burma. So I would begin with legal protections, as we talked about, right? But protections for minorities, that's really what we're, you know, essence. Secondly, we have to think about the connections around the world, which don't necessarily promote genocide, but do prevent a response. And let's be honest, I know it sounds like I'm Noam Chomsky here. But look, if you turned off the natural gas and oil pipelines to Russia, if you stopped the international arms trade, you'd really find things very different. You have to prevent, Rwanda is an interesting case because it killed with machetes, right? That's an interesting case. But where modern industrial society embarks on genocide, its coffers are usually filled with international economic wealth. Didn't take a lot of money to get machetes. Pol Pot isolated himself and killed people in traditional ways. So that's a slightly different issue. But in this case, prevention is really to cut off these regimes and these societies. And sanctions are important. I think sanctions, look, sanctions have been practiced for centuries. And particularly with an economy like Russia, which depends so very much on natural gas and oil. Okay, how else do you prevent it? Well, look, it's an old fashioned idea, but you do try to promote a political system, which is democratic and liberal, right? So democracy is rule of the majority. And we have to be careful about that, right? That the rule of majority is still with protections for minorities. Okay, I don't know if that helps or not. I can tell you, that's important that we consider all these things. I can tell you my final view. And I'm a very old fashioned guy that these are often inflamed, not just for racial reasons, but for class or social reasons. It's quite often, look, the Runga, not the wealthy people of Burma, it's quite often the poor. In Latin America, it's usually the poor who are victims of genocide, the descendants of slaves, Native Americans, the folks being murdered in the Amazon. So we really do also have to talk about societies in which resources are more equitably distributed. And we know historically, when resources are more equitably distributed, social ties are stronger and wider. It's the Adam Smith before the wealth of nations. It's the Adam Smith of moral sentiments. We don't have deep moral sentiments when we live in very unequal societies. And that's all the tender, right? I mean, the spark is, as you say, the spark is usually some charismatic leader. We might not think of him as charismatic, but the society does exploiting. I mean, very rarely do any genocidal leaders create a distinction, right? And Putin didn't create this distinction. He's drawn upon these myths and long term hatred. And the final thing I would say is we got to dump ethnic nationalism. That's okay. And the final thing I would say is we're out of time. No, thank you, Peter. I appreciate all your thoughts today. This is, of course, Think Tech Hawaii. Its history is here to help. We've been comparing and contrasting the Holocaust with other genocides and trying to get a handle on what the species can do about that. Thank you so much, Peter. Really enjoyed the discussion. Thank you. Aloha. Aloha. Thank you so much for watching Think Tech Hawaii. If you like what we do, please like us and click the subscribe button on YouTube and the follow button on Vimeo. You can also follow us on Facebook, Instagram, Twitter, and LinkedIn, and donate to us at ThinkTechHawaii.com. Mahalo.