Well, hello, I love a matchy match today. Right here on Run It Back, Fandal TV. My name is Michelle. We've got stadium insider, Shamsharani, a Chandler P, Lou Will on the end. Guys, look at us. We're sort of becoming one. How does that feel? Blackout. Like what? It's a thing, Blackout Tuesdays. I like it. We're going to, we're making it a thing. It's a thing. We're going to start with some basketball today. I don't love this first game, but I understand why we're talking about it. Warriors at Spurs. Yeah, no Steph Curry, 112-102 win for Golden State. Chris Paul with 19-9 rebounds. Adesis, Kaminga with 22. That was fun right there. And Wemby had his 27 and 14. But this was a very vintage Chris Paul performance. We've talked so much about him coming off the bench, play, and all that good stuff. But you don't have Steph right now. So Chandler, is he a difference maker while they waited out? Yeah, for sure. And this was nice to see Chris Paul have one of these vintage games where he was in control. He was the playmaker. He picked his spots where he could be aggressive. 19-9, if he could get anything close to this, when you have a season like this and you're kind of dragged through the media and everyone's talking about how it's not your year, you're older, the dynasty's over, you find anything to make a bright spot and give this team energy and a push towards the end of the season. And that's what Chris Paul, he did last night. So if they can build off this and he can continue to have these productive games, it's gonna help because again, we always talk about Steph and his use rate. Is he doing too much? Is he playing too much? He's banged up. When you get a balanced attack like this where Wiggins has 16, Kaminga has 22, CP 19, Thompson 21. When you get a balanced attack like that, first of all, it makes you that much harder to guard. And when you have Chris Paul controlling the pick and roll, everyone's moving, everyone's touching it. It's a fun way to play basketball. So as big of a hole as Steph Curry is when he's gone, when you can get kind of four or five, six guys in double digits and you can get Chris Paul playing like this to protect the ball and not turning it over, it's huge for them. And this is something moving forward that they know Chris Paul still has enough in the tank to go out and have games like this and carry a team to a win, even when Steph Curry is out. You wanna add anything, Lou? Are you saying enough in Chris Paul and what he brings that the postseason, this could be an X factor? Yeah, and Chandler can attest to this. When guys go out with injuries, you know you don't have a sub.  So this was great to see Chris Paul having a great night out there, especially being injured majority of the season. So to go out there and play with a lot of confidence and will them to a win against a struggling Spurs team, I think this was good for them. So Lou, I'm gonna start with you on this next one. Chandler mentioned Clay Thompson and Andrew Wiggins, another different starting lineup. They had their 37 points combined. This is their 21st, by the way, different starting lineup for this Warriors team. If this is what they go with, let's just start there. How does it change the outlook of this team in this final stretch? Well, this should be the standard, right? Like they expect Clay Thompson to play really good basketball. They expect Andrew Wiggins to be effective on both ends of the floor. So these are two guys that they pay handsomely to do exactly what they did last night. Unfortunately, it's been only a sample size for the season. They should be more consistent. So if they can get all three of these guys, Chris Paul, Clay Thompson, Andrew Wiggins all on the same page, along with the season that Steph Curry is having, they can be disruptive, come playoff time. Looking at the standing, they're gonna be in the play unless something catastrophic happens and Houston catches them five games behind to get in that 10 spot. They're gonna be in the play-in. So they're gonna have an opportunity to be disruptive to a lot of these different teams. So if they can get all of these guys on the right page, it can be scary for a lot of those top seats. Yeah, and Lou, they're only doing this because of the struggles, because of Andrew Wiggins having the season he's having, because the up and downs with Clay Thompson, because Jermaine Green being in and out of the lineup. So I'm sure they want consistency. I'm sure Steve Kerr wants the same starting lineup. And obviously with Steph Curry in there and Chris Paul, you then have Chris Paul carry in that second unit with those young guys and Steph Curry starting. This should be their starting lineup. These are their five best players. This is their team. Kaminga is the bright spot of the future. You mix him in with these championship DNA guys. So this should be their starting lineup. It's just throughout a course this season, injuries and suspensions and things like that. Steve Kerr's hand has been kind of forced to adapt and adjust and kind of go on the fly and make some changes. But when you see these guys clicking like they did last night, I think they had a 19-0 run to start the second half last night. They were moving the ball. Their calls and turnovers are getting on transition. They looked like the old school Warriors last night and that's without Steph Curry. So again, I think there's a lot of panic and because they've set the bar so high. The expectations are so high for this team, for these three guys in particular, that when they do have bad games, bad weeks, bad months and they're in the 10th seed, it's like, whoa, we never expected the Warriors to be here. But this should be their lineup going forward. Chris Paul being healthy, being productive on that second unit with Pozinski, with Trace Jackson Davis, who has been a real bright spot for them to find him at the end of the draft. This should be their method going forward. Yeah, the Steph Curry of it all shops. Look, there's no good time for an injury. But when you lose a guy like Steph at this point in the season, when you're playing for everything, it can't be great. He's now missed both games against the Spurs. Any word on when we can expect him back on a court? Yeah, you thought timing-wide, this was the perfect moment for him to miss these games, right? Two back-to-back games against the Spurs, but then you go one-on-one against the Spurs. Now you play the Mavericks on Wednesday in Dallas and Steve Kerr said last night, it's not expected that Steph Curry will play. So that would leave the Lakers against the Lakers on Saturday in LA as the next available opportunity for Steph Curry to make a return. I'm told it's not a super serious ankle sprain, but it is an ankle sprain that would cost him some time. So we're already gonna be at at least three games if he sits out Wednesday. So we'll see is a week a good enough amount of time for him to be ready to go by Saturday.  That could very well be something that we see as a preview going into the play-in tournament. But also, like Lou said, they have a five-game cushion, right? So if it's another two games, three games, another week for Steph Curry to get that ankle, right? I don't see a reason for him to rush. I think they know that they're in the play and Houston's not gonna make a five-game comeback with what's left. So if I'm him, I'm getting fully healthy.  So that to me is more important than him rushing back now and having this ankle linger into the play-in. So you're not rushing to get back to play the Lakers over the weekend? You just, it's fine. Oh no, I'd rather be fully healthy and play when it matters.  When this moment happened. I mean, listen, we praised his cat all the time every day and he had a great game last night, but Trace Jackson Davis put him on an absolute poster. And when you watch, he did kind of puff his right arm right here. So he did get away with little offensive foul, but that's a nasty play. My boy James Dunley reps him. He believed them. He got him to a great situation, AKA with his brother late in the second round and he's been a real bright spot for the Warriors. And this kid's got a real future. He does all the little things. He's so bouncy. And this, this is a nasty play. Luke, come on. This is on that man has a family segment. It's his own segment. Listen, I kind of want to disagree with you. I think this is clean. I don't even think he used it all for. This is a clean dunk. Let me check him out. Watch that right arm. All right, all right. That's a nice dunk. You're going to get, you're going to get, you're going to get banged on though.  That's the one thing a shop blocker has to realize is you're going to get dunked on 20 to 30% of the time, but 70% of the time you're going to have nice blocks.  It's part of the game. Yeah.  But yeah, the bench is ridiculous.  No, that's good. That's a pretty good dunk. Shom's pop said last night, unsure whether or not Wemby would be playing tonight. Any word on that? Good news for San Antonio. Victor Roman Yama, the expectation is he will be playing tonight in this back to back against the Rockets at home. And, and I think the ankle is doing well. It's, we'll see how many minutes he ends up playing. I think all that's going to depend on the flow of the game, how his ankle feels as the game goes on. But Victor Roman Yama said last night, he feels like he owes it to his teammate. He owes it to his team, the organization to be available every single night. So the expectation, the plan is that he will be playing tonight on the back to back. And you think about his last 10 games. These numbers right here, 23.4 points a game, 11.9 rebounds, 4.8 assists, 5.2 blocks, two steals, 49% field goal percentage, 41% from three point range over these last 10 games. You're starting to see him separate himself from the pack. And I think you have to put a lot of credit into how much he's actually playing. And he's finishing the year strong at a moment where a lot of rookies, some rookies are, are telling all, but you see him and Chet Holmgren right now. They're, they're at another level when you think about rookies in this class, as well as Brandon Miller. That rookie wall doesn't exist for Wimby. I'll tell you that. No, he's, he's out there. By the way, I just like to put it in the universe every morning, my Drupal double, my Drupal double. We're going to get one and I need it to be him. It's going to happen and I really hope it does.  Don't look now combined for 88 points, a win over Cleveland, Kevin Durant 37 and eight, Devin Booker with 27, seven assists and Bradley Bill with his 24. Devin Booker is back from the ankle sprain and they combine for those 88. If, if Chandler, we could keep this big three on that court, how much damage are they capable of doing come playoffs? A lot, a lot.  They're missing three starters and they've been dealing with their own issues, but this is what makes this team so scary. I don't want to play this team four to seven times in the playoffs when they have three guys that can go and get you 40 on any given night. And you have guys like Grayson Allen have a career year. You have Nurkits that's being, you know, just dominating the glass, having a hell of a season two, but this is what makes them the scariest team, I think, in the NBA. They're not the best Celtics, Nuggets. I think they're better teams, but when you get them rolling like this and you get them in the playoffs, you can't stop them. You can't stop everything. You can't double team KD or Booker because then the other two are gonna have wide open shots. When you have a guy like Grayson Allen who's arguably the best shooter in the NBA this year, he's getting more open looks than he ever had. And he hasn't, he's taken full advantage of it. So it's tough. And this is why Frank Vogel was sitting here after the game saying, listen, we can be really elite. And I believe him because they haven't been on the floor that much. And when they are and they have games like this, you're like, oh, damn, this is something special. So I hope they stay healthy because the Western Conference is stacked as it is. I think this team has a chance to be a top, you know, Clippers, Nuggets and them. I've said that for the last couple of months now. It's those three teams to me. And that's no disrespect to the Timberwolves. It's no disrespect to the Thunder. I just trust these three older experienced teams more and the talent, the offensive firepower that Phoenix, it's undeniable. So when they get going like this, I said, you can't take away every option. Someone's gonna eat that night and you can't control all of them. Lou, look, we disrespect, I don't even know that we're purposely disrespecting the Pelicans, but Chandler mentioned Nuggets, Clippers, Sons. And the Sons, as we know, we haven't gotten to see the big three over and over and over again. But for you, I know you're big Clippers, you've got them going through the West. You giving the Sons team a chance at all? Hell yeah, when they play like this, you gotta be mindful of what you're seeing. You're seeing three of the most elite mid-range players of all time and playoff basketball comes down to half-core offense. So when you've got three weapons like that that you can post up, you can play in the mid-posts, in the mid-range that can face up and make shots over guys, make tough shots and can play together, you're gonna have a very dangerous team. And so I look forward to it. My only problem with this Phoenix Sons team, we just hadn't seen enough of it. Can they be very dangerous? Sure, but right now we're looking at that lion and the glass. We need the glass to be broken right now and put some real fear in these teams and they gotta start putting a run together where you're like, oh, they're dead serious. This is for real team, because right now we've only seen it in spurts. We hadn't seen the consistency being a big three and a great team. But if they can do what they did last night and put pressure on teams like this, come post-season, it's gonna be a scary sight for anybody matching up with the Phoenix Sons. What's crazy, Lewis, they were getting smacked in the second. They were down, I think, 20 points, but they're all like, like I said, those three guys, I think they went on like a 45 to 10 run or something because you cannot stop them. So they're never out of a game. I don't care if they're down eight, they're down 28. They are never out of the game to that. So that is a luxury to have this much offensive firepower with really good role-players like the Royce O'Neill, it's like the Nurkish. So they're in a really good spot. If they can stay healthy, I'm telling you, Lou, like if they get the three-seat and the Minnesota Timberlodger Thunder are the six-seat versus the three-seat, you got them beating them in the playoffs? It's gonna be tough. It's gonna be tough like, it's gonna come down to half-core offense.  We need a bucket every time down the floor. Close your eyes and pick one of those guys, man. They're gonna get a bucket. Doesn't matter. Yeah, literally, it does not matter. And no, there's no three defenders on any team that can match up with those three offensive players. So like you said, it's crazy. And we're just now kind of seeing it.  So if they're all three cooking like they did last night, they're not losing many games. They're gonna have a mismatch. No matter who their play, one of the three of those guys are gonna have a mismatch. You know, a lot of teams, you might have one or two elite defenders. Nobody has three. You know what I'm saying? Nobody has three elite defenders. Somebody is gonna have a field day every single game in the playoffs. It is crazy how much more different it is when Booker's out there. Because we saw Durant over the weekend trying to get it all done and he had a monster game and it was a loss. And now you put Devin Booker back in and like you guys are saying, there's no stopping him. Kevin Durant, by the way, at 35 years old, let's not underestimate what it is he's been doing so far. He's averaging 28 and a half points. He's shooting almost 53% from the field and 42 and a half percent from three. Are we not talking enough, Lou, about KD in the season he's having? We're not, you know, he shouldn't be getting more attention. I think he's just, I think he's a victim of just being quiet, you know? When you look at Luca, you look at Joker and you look at what SGA's been able to accomplish over this season, they got really big, gaudy historical numbers.  Kevin Durant has only did one thing. He's been consistent and he's done his job and tried to give the Phoenix Suns an opportunity to win games each and every night that he's been healthy. So I think he's having a consistent season, but he's just a victim of other guys being a lot louder and a lot more gaudy. And it's crazy, it's 53% from the field, the shots he takes, it's not like they're easy. He's posting up off the block, he's stepped back, he's taken long threes and Kevin Durant, he's on the floor, he's played 58 games.  He went to the Warriors, he was the best player on that team. And I went to the Suns, this big three with Devin Booker, he's the best player on this team. So everywhere he goes, he's the guy. Everywhere he goes, he carries the load. And everywhere he goes, he puts up MVP first team, all NBA type numbers. So the guy is unbelievable, he's been doing it forever. And the fact that he's healthy and he's been in the lineup with a guy when Devin's been out, when Bradley Beal's been out, he's been the guy consistently for this team. So he's having a great year, he's having an efficient year. Like I said, 53 and 43%, that's insane because he's getting doubled. He's taking step backs. He's not taking high percent shots yet. He's shooting a high percentage. So we're not talking about him just because SGA and Yolkic and Luca are putting up these nuts numbers. But this guy is all NBA and we should be talking about him more. If the Suns were winning more and if they were healthy this season, we would be talking about them more because they would be a top three team in the West. It only takes five minutes of watching a Suns game to remind yourself, like, oh, that's right, he's fun to watch. I like watching Kevin Durant. Your biggest takeaway, Shams, from this win for Phoenix? I mean, I think we've hit on it. Kevin Durant, massive season. Just think about it. In 2019, he has the Achilles Tear and now he's in his age 35 season. He's got the most 30 point games for a player in his 35 years of age. Like what he's doing this season is remarkable, especially the last four games or so. He's been taking it up and even another level with Devon Booker out. I mean, 38 points a night, 54% from the field, 47% from three. So he's doing everything. This team is 19 and nine since January 8th and even though they played 41 games this year without the big three, just 24 with them. I think these next few weeks, it's like just building momentum with those three in the lineup. You have to have them and they've struggled down the stretch of some of these games and like Lou and Chandler know, a lot of that is because they haven't built that level of consistency. Well, now you have a stretch of about a month. If these guys can stay healthy, build some momentum and chemistry down the stretch of games, learning how to close games out. The last thing you want to do is put yourself in situations where these three guys are having to close games out for the fourth or fifth times in the playoffs. They can do it, they have the talent but a lot of that comes through chemistry, repetition and hopefully they get that opportunity over the next month. Wubble wrap and ice. Chandler, Cavs, look, it was a really good feel good story but unfortunately for them, they're five and seven and their last 12 bucks seem to have figured some things out. We know Boston's dominant. Is the Eastern Conference really just Boston and Milwaukee in your mind? Yeah, I do. Unfortunately, I love Jimmy Bigger staff and I love this young team. They just haven't been healthy. They haven't, you know, even last night, they have three, they have streus, they have Wubble, they have Donovan Mitchell and when they were fully healthy, let's not forget about that run that they went on where they got real hot and jumped all the way up to the second seed but they've just been too inconsistent. You know, they left a bad taste in our mouth and they passed you in the playoffs, losing to the first round to the Knicks and we thought they'd kind of make that jump early and they just didn't and they haven't been healthy, which is a huge issue, obviously. When you're missing three starters, you know how hard that is to go out there and compete in one game so Donovan Mitchell is there everything and now he's now missed, I think his 18th game last night to where he's had a great year but when you miss that many games, it's hard to get in a flow. It's hard to get in a rhythm and this team has the talent. They have the youth. I hope Donovan Mitchell stays around because I think this nucleus in this court can be very, very good in the future but when you look at this, it's even the up and downs with Milwaukee, the coaching change, Doc, they're a better team and the Boston Celtics are a way better team so I think they'll get a nice favorable matchup in the first round. It doesn't look like, you know, the Knicks or the Magic will catch them so they're gonna lock up the three seed there. Then we'll see and they can get a Philly, they can get a Pacers now with Mathera now so they should at least get that playoff experience advance in the second round. Then who knows? Then we'll see what happens but they gotta be healthy to have any sort of chance. The Donovan Mitchell of it all, Lou, is a bummer. It's his seventh straight game that he's missed but the bigger issue here is the fact that he's now missed 18 which brings back the old topic of eligibility for post-season awards. He is now ineligible for all NBA despite the numbers that he's had which is averaging about 28 a game, fifth in the NBA. All of these great numbers but he's out. So we're gonna talk that topic again and I'm sure it's gonna come up numerous more times but what are your thoughts on the fact that he is no longer eligible for that? I'm torn because as a former player, anybody can play really well for a stretch of games or for a stretch of the season and then they can fall off and then they kinda come back to Earth and reality sets in but on the flip side of it, it's like you miss a quarter of the season and you can come back and then your legs are healthy, your body feels good. So you can put a lot of pressure on teams that you can have really big nights because the NBA season, it comes down to traveling, it comes down to wearing tear, it comes down to so many different things outside of basketball that can give you a competitive advantage and so I think Donovan Mitchell is an all NBA talent but with these new rules, I don't know, I'm just, I'm torn because like I said again, you can miss 20 games, anybody can play well for 55, 50 games and put yourself in that position. The special guys that set themselves apart, those are gonna be the guys that play 70 to 75 games and they've been able to do it over the course of the season and being consistent and so I'm torn, he's an all NBA guy but the rules are the rules so it's a little weird for me. Yeah, I get it. Like I understand what the NBA is doing. They want the star players on the court, they want them to play the majority of the games and to reach these bonuses where they get paid, you know, fat checks at the end from their shoe deals.  Like when that happens, there's gonna be like this gray area where like, okay, like the threshold is one game difference and one guy's eligible and one guy's not and that's gonna happen. But again, those are the rules, they've been established, guys know this so I don't know if it's a great thing that you're telling these guys if you wanna be rewarded for all these rewards after a season you have to play through certain injuries because then if they get worse and then you miss the playoffs and you missed the most important games of the year that's gonna be a whole another bag of worms. But again, it's like Lou said, you can get hot for two, three months but it's the guys that play the five, six months that play 75 plus games that they deserve it more. So if there's a guy that's averaging 26 and six and five and he played 80 games, he's probably more deserving than Donovan Mitchell who's gonna miss 20 or so games. So I can see both sides but it's gonna be really annoying when one guy plays one more game than the other and that guy gets rewarded all NBA. Yeah, mathematically it's gonna happen. And then we're gonna have to have that conversation as soon as it does. Guys around the league we go. Yolkich, woohoo, 35 points, 17 rebounds, 12 assists, leads his team to a 21 point comeback win over Toronto, 125, 119, 21st triple double for Yolkich and 126th of his career. He also had six deals if you wanna keep track of that as well, but I'll go ahead and say it Lou. We're numb, I mean, I know I am because when I go and open up my FanDuel app for me it's a given that he gets a triple double which is a good thing, but it also feels like what a world to live in that we assume he's gonna get another triple double. Are we a little bit numb? No, we're very numb. And Shums, do we have scoops? Do we have scoops? We don't have anything. It's an alert for me to call someone though. So I'm gonna make that call after this show. It's coming. We're working on a scoop. All right, but listen, we're so numb to it. I think this works in SGA's favor for MVP. Call me crazy, this is gonna be a hot take. But because this is so normal, I think we're bored to it. We're just like, eh, okay, another great game from the Joker, this is what he does. And then if Shay Gilgis Alexander does anything remotely close to this, we're like, yeah, that's what I'm talking about. The young guy in OKC Thunder, I'm turning the corner. That's how we are. I think that's just how we're programmed to be. He's playing at such a high level, so consistently. It's just like, okay, another big night for the Joker. That's insane though, Chandler. It has to be insane to think that way. I mean, the voter fatigue thing, it's real, right? Like you just, it's the same stuff every single game. And it's unbelievable. I will call Luke crazy, because I think it's his yokutsu MVP. And I think it's not even close. I think he's gonna win it, and which is insane, it's gonna put his resume at such an insane high level. But we do get numb to it. We get numb to all these new historic offensive stats that we're seeing every single night. Think about it, we're talking about a quadruple double. Like we're just waiting on it to happen because we know it's gonna happen soon. It's crazy. So when you see 35, 17 and 12, like, and it's consistent, this like, there's guys that have had great years that this would be their best game of their career. And this guy does it every single night. Like I would have got a max contract off this one game. I would have got a channel. I would have never even stepped to stat line like this. What would you do for a 35, 17 and 12? Like I would, I'd give my left arm. I would have my agent just play this game over and over again until they've paid me a huge bag. But you're right, we are getting numb because he does it so often. He does it efficient. Because damn, you're shooting 60% from the field. Like it's unbelievable what he's doing. Like I said, that stat line, that would rock my socks off if I saw that a couple of years ago and I saw it. And now it's like, oh yeah, Yoka's played good. How many did Murray have? And I thought, oh, Monk Porter, it's crazy. I don't even bat an eye at this cat stats. God, we're brats. Sports fans are brats. We just spoiled and rotten. But guess what? Another guy that's got a lot of numbers coming up next because it's Luca. Are we bored with him too? He had 27.12 rebounds, 14 assists, his seventh straight 20 point triple double. That ties the NBA record for consecutive 20 point triple dubs, snapping a streak of six straight with at least 30 in a triple double. They did beat Chicago very, very easily, but he checked out midway through the fourth quarter. Lou, should J-Kid have left him in just to, you know, get a couple more and get that 30? Nah, nah, get him out of there. Cause chasing that, listen, I've been on the other side of this playoffs. I was on the Sixers and they were up about 20-25 with about two and a half, three minutes to go in the fourth quarter of the game one of the playoffs and Derrick Rose towards ACL. And that eventually led to us upsetting the Chicago Bulls in the first round of the playoffs. So you don't want to leave a guy in and risk it based on numbers. Luca got plenty of stats. He doesn't need another 30. So no, get him out of there.  So I think there's a bigger picture here for Luca and the Mavs and look, he's got plenty of time to break and to set his own records. They're thinking post-season. This is the time of season where you don't play around these type of things. You get the win, you get off, you get ice, and you get ready for the next game. You try and get the highest possible seat. This isn't about chasing records. And the Mavs faithful, they got on me the last couple of days cause I said it must be exhausting playing with Luca. It's not an insult to me. Look at everybody there too, Sandler. I'm just saying, it's not an insult to Luca. I think the Mavs to be the best team they could be, they have to be balanced like the Warriors were last night. They have to have Gaffer. They have to have Jones, Tim Partway. They have to have all of them contributing and wanting to feel wanted and to needed to have the ball.  Him and Jokic are so similar, just the way they can't be sped up, the way they get everyone involved. And by the way, when I say exhausting to play with them, I'm saying watching the same, my best years with James Harden, it was exhausting to play with him, just watch him do the same things over again. It was awesome. And I average, most points I ever did playing with the guy, even though he was very ball dominant and his usage rates was through the roof. All I'm saying is for the Mavs to reach that next level, I think they have to be more balanced and they can't depend on Luca doing this every single day. It's hard to do. It's really hard to do for one individual to sustain this. So, listen, I'm hopeful that he's... You will see somebody in every show. I think he's just... Who is he wrong? He's not wrong. I really don't think I'm wrong in this case. I really don't think I'm wrong. You're not wrong. Chandler's right because the Mavericks, when they win at a high level, it's when all these guys contribute and they're just not gonna be a championship team. If it's just Luca Dacic going for 30 point triple doubles, that's not their recipe. The recipe is Kyrie Irving, Tim Hardaway Jr., Lively, Gafford, PJ Washington. They have, I think Chandler said it the other day, they have probably as deep of a team, well-rounded of a team as they've had around Luca Dacic in the last few years and he was a big proponent of getting PJ Washington in that trade. And I think Luca Dacic wants to win and I think this team is just about figuring out and really handling the rotations the right way, tightening up on defense, but I tell me Chandler's right. And by the way, when they do have this balanced attack and when the floor opens up like that, Luca's gonna naturally still have these stats with everybody else getting theirs too.  So when like you just said, Shom, when guys are in the balls, moving and zipping and you're playing offense like you're supposed to do, everyone's touching it, everyone's happy. That's when you start winning at an elite level. So back to the point, imagine the message that J-Kid is sending if he leaves Luca in there to try to get a 30-point game, right? You got a big lead, let somebody else get theirs off. Get out of here, Luca. You know what I'm saying? This is a team sport. Yeah, J-Kid leaves him and he's basically saying Luca's individual records are more important than us winning and being healthy in the playoffs, which you just can't do. You can't risk it. Shoms, now we're just gonna bug you as soon as you leave us to find out what it is you're about to do and call back, but we say goodbye. We'll see you bright and early in the morning. Thank you, sir. We'll be back with a little who you got with Chandler and Lou on Run It Back. Little who you got for the guys. Two choices, you pick one first up, better rookie season. I'll be honest, this feels like a win-win for everybody involved. So Victor Wemmonyama versus Tim Duncan. Now, Timmy D was a little older. His team was a lot more established when he got there. We know Victor's a kid, but let's go. Talk to me. No, no, no. Don't do that. What are you, Victor's agent? Don't do that, Biddle. You're trying to, you're leading the witness here. You're leading the witness. I'm just pointing it out in case y'all forgot. It wasn't on the screen. Lou? It's clearly Tim Duncan. It is clearly Tim Duncan. He was an All-Stars rookie year, you know, allowed them to get to the second round of the playoffs. Victor Wemmonyama is not going to sniff the playoffs. Went on to win a championship in his second season. Maybe there are some light at the end of the tunnel for Victor Wemmonyama if this is the standard being set. But this is clearly Tim Duncan. Yeah, it's a different era, it's a different time. But the guy also played every single game. He played a few games his rookie year and played damn near 40 minutes. That matters. And they won, like Lou said, I think they went, they beat Phoenix in the playoffs and they went on to win a championship their second year. I don't know, I know we're talking rookie year, but I don't think San Antonio's going to do that next year. But as special as Wimby is, even the numbers alone, the numbers alone, Tim Duncan's are better. But the future is bright. Who's in town, you know, Michelle? The future is very bright. Like I said, very rare that we have a choice on this show where I like both of them. So win-win, woohoo, goes first go. Moving on, more impressive streak, love a good streak. Demonus, a bonus, you are up on this one. Your 46 game, double-double streak, that's kind of insane. Or Luca Doncic's seven straight, 20-point, triple-double. But six of those were 30 points. Chandler, you're up first. It's a tough one, because they're both really, really insane. The 46 games, that's damn near half a season. This cash is throwing in double-doubles every single night. I think it's harder to do what Luca's doing with that clip of triple-doubles. And, you know, six of them being with 30 points and it could be even more if they weren't, you know, blowing teams out. Wow, this one's tough though, man. This one, right? Again, stats are so magnified now where there's a little bit watered down. I feel like with the usage rate you could argue either way. Wow, Lou, I don't know what to go here with. I know, Lou. Okay, listen, he's supposed to get 10 rebounds every game. So I'm going with Luca's seven straight, 20-point, triple-doubles. So bonus is, yeah, you can easily get... He's flackin'? Is that what you're saying? He's flackin'?  You're gonna get four or five rebounds just by being in the right position. So for Luca to have 20-point, triple-doubles in six or 30s, give me that, I'll take that for sure. So bonus didn't have one game where he didn't, or one game where he came out of the game, 46 straight is a crazy clip. But you're right, I think with Luca, with Luca, the triple-doubles and the six straight, 30-point, triple-doubles, that's literally a record. It's never happened before. That's gotta be more impressive. Yeah, it's kind of silly. We need some merch. All right, moving on, we got some numbers for you guys to consume digest process. Here we go, better season. SGA or the 13-14 MVP season. Hmm, for KD. This one's interesting, guys. I was reading it as I was doing it as well. Chandler, you wanna go first? Yeah, what's interesting here is again, I look at what happened that season, which I shouldn't because the award is given before that. And the Thunder that year went to the conference finals, and I don't think the Thunder are gonna do that. So I think KD's season was better because I think he's gonna go further than I think the Thunder go. They're both in same season. And by the way, I think SGA is going to finish second in MVP voting behind one of the greatest centers of all time of our generation. So again, you could go either way here. I like that the Thunder went to the conference final. I think these awards should be given after the season. So when you see what actually happens, because winning is a huge part of definitely the MVPs. How about rookie of the year, whatever, not mattering. MVP matters winning. So like if the Celtics happen to blow through the playoffs and then Tatum is unbelievable for those two months in the playoffs, I think he should deserve some real MVP consideration. So it could go either way here. The numbers are spooky similar here, but I have to go, Kevin Grant.  I think Kevin Durant was always Kevin Durant, right? Like coming into the league, he's always had an expectation of excellence. We haven't had that for SGA. People are still kind of surprised in shell shot that he's having the season that he's had. He hadn't had the fanfare coming into the league that Kevin Durant had. So that MVP season, I think was expected. And I think what SGA is doing, it's an amazing feat for him to be put in a position to even be in this MVP conversation. So I'll give it to SGA. I'd love to go back, have some researcher person do this and look at all the MVPs and then take the context of how those teams finished ultimately in the post season and see if it would have been the same. If we did what Chandler said and gave it after everything. Which I also don't disagree. I know, I wonder how many of those would not have worked out the way it did. All right, more important player for the Lakers as we head down this final stretch. I'm just reversing the order on everything. Is it LeBron James or Anthony Davis, Lou? It's gonna be AD. You know exactly what you're gonna get out of LeBron. You know, LeBron is gonna show up and show out. AD has to be the aggressive bully that we've been seeing in these last couple of weeks. You know, maybe he's gotten to a point in his career where he's bored with the regular season. You know, you kind of see that with veteran guys. If they've had success at the top of the mountain and won a championship, you kind of coast through the regular season until it's time to start revving it up and you're turning that corner. You flip that light switch on and it's go time. And I think that's what we're seeing from Anthony Davis. So for them to move forward, for them to be the dangerous team that we think they are, he's gonna be the most important player. X Factor. You agree, Chandler? Yeah, I do. I agree. And I think LeBron, again, LeBron's not in his prime. Anthony Davis is. This is his team and he showed the other night. He can dominate a game. He can impose his will. He can clean up the glass. He's so talented. He's gonna score his points. He's gonna get his rebounds. But he can impact the game so much more defensively, altering shots, helps side defense. So I think he needs to be the guy where, again, like Lou said, LeBron's gonna get his. LeBron's gonna get out in transition. He's gonna get downhill.  He's gonna have 25, eight and eight after every game. Doesn't matter. Anthony Davis is the X Factor where he's gotta have these 25 and 15, 30 and 20. He's got Anthony's capable of doing that. And we always talk about it. We criticize him a lot because we know it's in there. We just want to see it more often. This last one, I feel like we're trying to start a fight here and our producers chose violence this morning, but we're gonna go historical. Peek Kobe versus Peek T. Mac. Got a lot of numbers for you if you want. They're right there below Lou, but. Oh my God, I didn't even see this one on the script. I know and it's it. Yeah. Lou said, I could already tell from Lou's face what's happening here. You want me Chandler? Let's let's hear what you guys say. My face. Are you not an Orlando Magic fan? I had every T Mac shoe in the book.  I love T Mac, but Kobe is one of the greatest. He's a different dude, multiple champion, played defense. It's not even close and T Mac was special. T Mac was so tall and long he go get a bucket. He had crazy highlights, but yeah, this one I feel like is clickbait here, honey. I want to touch this one. No. Oh wow. I'm just saying, go Kobe and leave it at that. Kobe, Kobe. All right, it's all Kobe. Well, you know what? We just wanted to shut out T Mac because the numbers are impressive, but whatever. You ever have the T Mac shoe? Lou, I used to wear one blue, one red one. Remember those T Macs? And you ever have those for so dope? I was a Nike guy through and through, but I love watching the guys playing the T Macs and then they were missing matching. The padded leather blue ones you're talking about with the three strikes across the top. By the way, did you guys donate those? What? Did you know he's cut? Him and Vince Carter are actual cousins? Yeah. Yeah, they're blue. Did you know the sky is blue? What's wrong with you, bro? I don't know. I thought I just dropped a little thunk bag. I was always late for the party. Did you know Trace Jackson Davis is Dale Davis' son? Yes. Anything else you want to share? Go. You know what? Let's take a break. We'll get more fun facts out of Chandler on the other side of this one. I don't want to run it back. Returns. I'll just shake it across the road.  All right, you buying that, Charles Barkley? Woohoo, he said that to fix the depth problem that Boston has in their bench, they should bring Porzingis off the bench, Chandler. Buying that? No, I'm not buying that. I think he's had a great year. He's putting up great numbers. He's perfect compliment to be able to pick and pop, stretch the floor, space the defense out. I think what they should do is sign someone like a Malik Monk, sign the deeper, better bench. I don't think you disregard KP for having a great year moving him to the bench. I think you keep their starting lineup as one of the more elite, just like Denver. Their bench isn't great. You're not just gonna all of a sudden start Aaron Gordon. So I disagree with this. I think KP's having a great year and I think their starting five is good enough to win a championship this year with their weak bench. So I don't see the need for this. Only time will tell. Chandler Liu, you're up next. Carmelo Anthony believes that only Anthony Edwards and Jason Tatum could be the faces of the league. What? Are you buying that? Nah, I'm not buying that.  Luca is still considered a young guy. We don't talk enough about Paolo down in Orlando. It's a lot of guys that can potentially be the face of this league. So these two guys is a great start, but not the only two. So nope, not buying. Yeah, that word only is a little bit weird. Chandler Magic coach Jamal Mosley said Paolo Bankero can pass like LeBron and score like Carmelo. You buying that? Yeah, this is what David Visdale said about me when I signed in Memphis too. That's what head coaches do. They gas, gas, gas. Listen, I love Mos. I think he just signed up for your extension today too. So he's gonna be there for a while. This is what he should do. Gas up your franchise player.  He can, I see similarities in those two guys. But whoa, like let's pump the brakes a little bit, but that's Paolo.  Well, yeah, it beats the alternative in saying nothing or worse. Allelu, you're up next. Tyloo said that Jalen Green and Cam Whitmore remind him of when T-Mac and Vince Carter, who are cousins, got to Toronto. Are you buying that comparison? I'm not buying in no disrespect, but I'm not very familiar with Cam Whitmore. As I was with T-Mac, when T-Mac came in the league and the things that he and Vince were able to do in Toronto, like they were so good together in Toronto. Them being cousins was a footnote. They were, they were establishing sales individually. Like, so it wasn't a, that's Vince Carter's cousin. No, like, no, that was Tracy McGrady. You understand what I'm saying? And so Whitmore here, he got some more proven to do. Jalen Green got some more proven to do. If we're just talking about them guys playing over the rim, okay, cool, sure. But the totality of their talent and what they were able to accomplish, nah, I ain't buying. If we find out that Jalen Green and Cam Whitmore are cousins, my head's gonna explode. Okay, now that would be breaking news, Chandler. If you could bring that to us tomorrow, that's huge. Work on that. Maybe that's what Shams' call is. Chandler, Gilbert Arenas. Oh, I love a sentence that starts with Gilbert Arenas says, LeBron was a rental for greatness and that his quote, Laker resume doesn't have a statue. You buying that LeBron ain't getting a statue in LA? Well, I'll say this, I think if he signs an extension, he stays there longer, it's gonna help his case, but no, that's a pretty long rental. He got a championship, he's broke crazy records there. I think he's gonna get a statue. He's arguably the greatest player of all time. Me and Lou think he's the greatest player of all time. So the fact that he's been on the Lakers for this long, brought a championship to them, has put up insane numbers. He's doing at 39 years old, but nobody else will probably do in that Laker uniform. In any uniform, the guy's getting a statue. I gotta follow this, I'm sorry. You don't think the Los Angeles Lakers are gonna claim the all-time leading score? They're gonna let Cleveland claim him? No chance, he gets a statue. And there will be no spelling errors on his statue either. Thanks. This will be perfectly the... There's no way the Lakers let's just walk out of their building, no way. Can we just spelling check all statues from now on? It's not hard. The mistakes on the Covey letter are disgusting. James Harden, channeler, said that he would shave his beard for a championship. Are you buying that? Have you seen pictures of James Harden without the beard? Unrecognizable. Listen, I'm pulling for the clippers because I would love to see this. Yes. So you know, I'll shave my beard if the clippers are just shaving their beards. I would love to see it. And I buy it. Listen, James Harden is the first ballot hall of famer. All he needs is this championship. So I love him to go off the beard. It's got a great opportunity to clover first. I'm buying it. I think he would... Oh, now he has to. That was for sure. He has to. He has to. Plus, I want to see it as well. I'm with you. All right, we'll take a quick break, wrap things up a little more, run it back when we return. And on all the NBA buzzer beater ankle breakers and Tomahawk jams with Fanadule, America's number one sports book. Right now, new customers get 150 in bonus bets guaranteed when you place a $5 bet. The app's easy to use and there are so many different ways to bet like live same game parlays, find bets in the new explore tab, make a parlay in the parlay hub, the best way to find popular parlays and more. So download the app and make your first bet and lay up Fanadule official partner of the NBA, Manana Darius Miles joins the show. Enjoy your Tuesdays, guys.


Hi, I'm Kondha Reddy from Indian Institute of Science. I'm a PhD candidate at the video analytics lab at the Department of Computational and Data Sciences. Yeah. So today we're going to talk about adversarial images. So there are a lot of questions we get answers from this talk. First of all, I'll try to introduce them. So what are they? And then why is it a worth the effort to know about them? And then we'll go a little more deeper. Why do they exist? And then there are recent efforts to generate and then analyze the deep-learned models. And yeah, if it is a problem, what can we do about it? And then we'll see a couple of approaches to efficiently handle them. So what are adversarial images?  But they are not normal to the models that you train and the fool efficiently, all those models. That's the definition of adversarial images. So for example, on the left you see a normal image. When you present this to one of these classifiers, something like VGG, ResNet, you see a label predicted as sheepdog. That's correct. On the right you see a little perturbation added to the image. And then you see paintbrush and absurd. So these kind of images are adversarial images because we don't have any problem recognizing the object here, whereas the state-of-the-art object recognizes how. So if you can't see properly the perturbation, I have a different image. Yeah, the resolution of the projector. So I hope you see some of the perturbations added to the image. So the objective of perturbing is basically this is a specific objective to make visually imperceptible and then cheat the classifiers. That is a pair of objectives when you construct a perturbation. So this is what adversarial images are about. So why do we need to study them? We need to study because they misclassify all these object recognizers with confidence. And you can make them to fool to whatever label you want them to fool also. So here is an example from one of the recent papers. On the left you see an image which is Panda. And VGG classifies with the, I may not be correct when I say VGG, but one of the state-of-the-art classifiers correctly predicts with 57.7 confidence. And you just add this noise with very small magnification. And we don't have a problem to recognize this as Panda. But the model says it is a given with 99% confidence. This is something very severe. So we need to study and understand them. If you're not convinced, I'm sure pretty much most of us are really into driverless cars. Let's take what can happen. So if it is one of the into future driverless cars, it sees a signboard, something like this. Obviously, there is a CNN recognizing the world around it and try to infer something out of it. Let's say this is an adversarial image. And if the CNN thinks that as a no-limit sign, that's something chaos. You want your systems to be reliable. You want your systems to be stable to perturbations, attacks, any other noise that is there in the acquisition process, et cetera. So that's a severe problem. I hope I convinced you. So it affects a lot of things. It affects deployment of the models right from simple access granting to, let's say, defense applications, autonomous driving, medical diagnosis. Everywhere we have a deep learning model, which is susceptible. That's something about the deployment of the models. So that's not the end of the story. So they demonstrate a very peculiar property, the adversarial images. That's called transferability. So I'm sure some of you might be thinking that it's not the case always unless you have the information about the target model that you are attacking. You're not safe even if you hide your model from any attacker. So that's what transferability is. You don't really need to know the target system or you don't need to have the data on which the target system is trained. That's an interesting property, which we generally refer to transferability property of the adversarial images. So that makes the problem more complicated. Let's say you have a target CNN trained on this database. So you hide this. You don't show this. But in my laboratory, I'll create a model CNN with a very different database. Of course, it should match the same distribution. Like if this is recognizing cats and dogs, you can't train this with humans. So as long as you have the same distribution, the samples may be different. And the model architecture is completely your choice. And you go about training this model and build adversaries for your model and your data and create perturbations like these adversarial images and now take to the target system. And yet, you can fool with significant confidence and significant number of times. That's what transferability is, which is also severe. The story gets bigger and bigger. There is recent work discussed probably a couple of weeks back in CVPR. What they show is, so there is a concept of universal adversarial perturbation. So one single image shown in the middle can fool different images that belong to multiple categories. So you just add this guy and you get to perturbation adversarial images. You see all these red ones are misclassifications and blue ones is classification. So most often you see getting misclassified. So that's what we call universal adversarial perturbation. So you don't need to really know about the image. It's it need not be specific to the image. It can be one universal perturbation when you add, which is almost imperceptible, but can cheat all this state of your neural networks that are doing object recognition. So there are models which can fool segmentation and other aspects of vision also, but for this talk I'll be concentrating only on the recognition models. So just to give an idea to what extent they can fool, these are the numbers copied from the paper. So if I train a VGZF in my laboratory and attack all these models, this is how the fooling rate is observed.  And if you don't know about the target model, this is what you get. These fooling rates are significantly high out of 100 times, let's say 40 times, it fails. Yeah, it's not worth it. Similarly, all this diagonal, sir, when you know the target system and off diagonal, sir, when you attack a foreign system where you don't have any information about the model. So these are significant. So yeah, story gets bigger and bigger. So at least this model, they use a subset of training data to get this particular universal perturbation. So in our recent work from our lab, we showed that just there are objectives that don't require any data to create a universal perturbation. So without using any image, be it a random image, be it a image that is in the distribution of the target data, anything. We don't take any images and just go about perturbing without the knowledge of the data. So it's called data independent objective for fooling systems. Again, this is the same table from our paper, which is quite significant. So I hope I convinced that this is a severe problem. And then let's go to how are they generated actually? Yeah, we observe that there is a concept of adversarial perturbation. Why, how, all these things will continue. So let's take a linear model. So it holds for any data, any machine learning system. It's just that we work on visual data. I've been talking about the CNS, the two recognition, but it holds for any ML system. So let's take a case of linear model where linear model is W transpose X and X star is the adversarial image or adversarial data. When you input adversarial data, obviously have W transpose X the clean part and delta X is the perturbation part. So the activation or the output grows by this amount, W transpose delta X, right? So if you want to maximize this particular activation, the growth in the output, obviously it looks like if the sign of the delta X is matching with W, it increases the growth. So that is one thing. And if you have a limit on the amount that the corruption, the noise they're inducing has, let's say you have a limit on delta X, which is epsilon. So each of the component can be modified only by delta epsilon. And on an average, the magnitude of W's, the component of W's have magnitude M, right? And the dimension of W is N. Let's say this is the case, the activations grow by NM epsilon. So it's linear in the dimensions. We all know all these models work with higher dimensional projections. Obviously, if you increase N, the activations grows by a lot. So this is one simple example, what can, which can tell you with an infinitesimal change in each of the components, you can cause the systems to generate a large change in the output. So this is a case of a linear model. Let's take the case of non-linear models because like none of the models, we work linear and a lot of complex models have extreme non-linearity. So let's take a minute aside to understand the gradient concept when we train a neural network. So what is gradient? So basically, when you train a neural network, you have a cost function. Let's say J is the cost function associated with neural network with parameters theta and input X and Y is the target. So we generally use this to update the parameters in the negative direction of the gradient. You compute the gradient and there is a learning rate and you move the parameters by that much amount. What if I compute the gradient with the input X? What does it say? So definitely it will be of the same size as input. If that is X's image, delta X, which is the gradient of the cost with respect to the input also looks like an image of the same size. Let's say two to four, two to four, three, the typical size. It gives the direction in the image space in which the cost for this particular prediction increases. That's what it gives. Gradient gives the direction in which the function increases. Here the function is cost, right? Imagine two to four, two to four, three dimensions where one of the points is your data and you're doing gradient ascent in that space at this point X. You just move a little bit that gives a variant of X where the cost is a little bit increased. Let's take an example. This is your X, X naught. So the cost computed at this particular example is, let's say 0.76 and the confidence given to the character class which is soap dispenser is 0.546. Obviously, out of all the thousand, if it gets this much confidence, the label, the system is going to predict this much. I compute gradient. This is how it looks after magnification. So it will be very small. So this is the gradient computed at this particular sample. Let's say I add this to X naught and get the adversarial component, adversarial sample. So this looks like almost the same, but if I compute the cost now, it will be 1.18 far away from this and confidence to the true class is extremely low and then some other class is increasing. Now the system is forced to do a prediction which is wrong. This is what the gradient can do. It can give the directions in which the loss can increase even without changing drastic amounts in the input. So this is the case of a nonlinear model. So these are some of the examples. So this is the gradient computed for all these in the middle row, middle column. And these are the X naught squares, sorry, stars, perturbations, almost imperceptible. So just to summarize this particular gradient, using gradient information, so you can compute the gradient information and then take the sign and you can have a limit epsilon the contamination that you're adding at each pixel, right? So your third sign also you can add, but it doesn't give you control on how much contamination you're adding. So this is the contamination you're adding in the direction of the gradient. So this gives you adversarial image. So this is called fast gradient sign method, FGSM, 2015 work by Goodfellow. So this is how they generate perturbations that are imperceptible and can cheat CNNs efficiently.  So this is a simple illustration of loss in 1D. So this is where you're currently operating with this X and JFI. You do gradient ascent, you get a direction and then you just move a little bit to see this much and you add and get this particular thing and the loss is going to be increased. So now because of these added perturbation, you're forced to operate at this point where your conferences to all the target classes change. So this is one way and which is very familiar, very efficient way because computing gradient is cheapest of operations you can think of. There are way better, way complicated approaches also. So most of them they use optimization techniques to craft the perturbation with two constraints. One is visually imperceptible. So it has to be limited by the magnitude. That is one constraint. And the second is misleading the classifiers. So with these two, if you can optimize to get such a delta, you're able to get a perturbation in a different manner. So there are a list of works. Basically these two are the constraints they impose to get these perturbations. So this is one particular example because it is so nice I just wanted to give a slide. So this is a CEPA 2016 work. It's a simple intuition. You just push the sample to the boundary, nearest boundary. It's a simple binary classification where this is one class and this is another class. You're operating at this point. You just add a perturbation to move this particular projection just to the boundary. So you compute such a delta here in this case R, which can find out the nearest boundary and then project it and add that particular R. So it's so simple, at least for the binary case. And they show that this is easy for complex CNNs also.  So that is how we can generate adversarial perturbations. So that's okay, fine. You establish ways to find out ways to cheat all that. So let's go a little bit deeper. Why they exist in the first place. So obviously we've been thinking that all these models are extremely non-linear because you have, let's say, resonate hundreds of layers and then everywhere we have a non-linearity, which is a ReLU sort of thing. So you've been thinking that, okay, my model is extremely non-linear, can fit to any classification function, but just let's just wait. So all the way we've done simple linear approximations to get the gradient and then did X plus the gradient part, right? So let's say something about non-linearity. Maybe the models are not that non-linear. The fact that you're able to craft a perturbation by assuming linearity for the loss function, say something about our assumption, the commonly held out opinions of non-linear models. So that's something to be investigated, but definitely if it is so non-linear, you would not be able to fool with the simple gradient information, right? So that's something to ponder about. There are a lot of other speculations, hypothesis empirically proved, but not mathematically. So some of them are listed here. So what I mean to say is, I cannot really prove with the rigorous math, but a lot of experiments can be done and showed. So insufficient model averaging or regularization. Yeah, probably, see that's because you're operating in like hundreds of dimensions. Let's say typical CNNs where the FC8, the final layer fully connected is 1024, 4096, very high dimensions. So you don't have enough data to project and then learn, right? Obviously, if you throw one million, which is ILSVRC data size, if you throw 1.2 million into 4,000 dimensions, it's still sparse data and you're trying to learn something out of it. Obviously, your model is very good. When you take a sample that is very similar to the training data, that's where the generalization comes. See these adversarial images can be thought of, test of generalizability, right? Because you don't have any problem recognizing the dog, which I have shown, but the model sees something else because it is trying to interpolate from its knowledge and failing badly. So insufficient regularization, all that. So discontinuous mappings learned by models. This is exactly what I was trying to say. So you have very sparse data and you learn only at those areas and when you project a novel sample, which is adversarial, I don't know where it falls. Maybe it is falling very far away or very close, just that the interpolation of the model sucks. It is committing a mistake. Or there are some fundamental blind spots in the learning algorithms. So whatever we are using today, a simple basic back propagation. Maybe it's not so efficient to handle all these things. So there are a lot of speculations. People are regularly looking for explanations for this, but this is the status as of now. That's good. So we know that there is a problem and nobody is safe. So what can we do about it? So there are a lot of simple ways to, you know, at the outside it looks like noise, right? So people think that, okay, it's just noise, why are you so worried about? Let's do some mean filtering, Gaussianize. That can get rid of the perturbations that are added. Maybe your system is now behaving normally. So people started with all those empirical ways, like foveation, like you take crops, multiple votings, and then see what the majority labels is. Yeah, as I was telling image blurring, or some JPEG compression, something like that. So this is a set of primitive operations, not intelligent. And there are some intelligent ways also. So we can impose some, I'll explain, we can impose some constraints during the training itself to make the models robust. We'll see. So as I was explaining, these are very small, low-level image processing techniques, I would say. So foveation is, so if at all, all these perturbations added in useless areas, let's say all this is not required really to recognize the bird. So I'll just take crop of the object and then feed. Some cases you get good, because you're removing all this extra Delta X part from the X, so that can help you. So you can take crops, flips, everything, all that, and take a voting of the final labels predicted. Though you get like 10, 15%, not sure that you will be ending up with a right prediction. Similarly, JPEG compression.  Maybe you're introducing something else, but not adversarial noise. So that's also one of the cheapest techniques, something like that. So just to give an idea, so this was taken from one of the archive papers. So versatile images, non-corrupted, top of accuracy was this thing. And if you just do three-crossed average filtering, you are recovering 73% of the images. So I must say this, the table has to be taken with a caution. There's just MNIST and CIFAR kind of smaller dataset. Yeah, sorry. Let's resume. So these experiments were not done on ILSVRC kind of big data. So yeah, that's the added salt. Basically, in my personal opinion, they're not going to save you. So like in that case, what can actually save us? So this is a procedure introduced by again, Ian Goodfellow in 2014. They call it adversarial training. So what exactly is this? So you know that the model's interpretation is bad when you present adversarial images. So why don't you introduce this kind of data during the training itself? So that's the funda. So this is what is happening. So you give X in the corresponding label and try to train the CNN. This is what is happening. So during the mini batch training itself, you compute the adversarial images for half-cooked image or sorry model. The model is still getting trained. So I would use half-cooked one and then you compute the same adversaries and you take these guys. So of course it does a pretty bad job of classification. But now you change that label to the right label and you say, no, no, no, this is not paintbrush. This is still a dog and then present it as a training data. So that's how you introduce this kind of, novel data to the system. And you just use the same data along with the original data to train. Now you have better data, which is adversarial since it is not, it is becoming normal data to the model. So but it has a couple of drawbacks because it doubles training time because essentially your data is getting augmented because at every mini batch you need to compute the adversarial set for some of the batch samples and then make them augmented. And let's say you are using adversarial samples of one kind. So it may not be intelligent to handle adversarial samples of other kind. So that is one drawback. It knows whatever adversary you are introducing but it may not know all other ones. So there are pretty other ways also. I'll just give one of the interesting ones. So this is called contract to penalty. So basically how to look at these things. So adversarial perturbations are questioning the stability of the model, right? When do you say system is stable? So for a small change in the input, you don't expect greater change in the output but this is what is happening. So the system is unstable. So what to do to make them more stable? So you include another penalty where you don't see a lot of change from layer to layer. The power was not connected. Sorry for the interruption. So we were at designing specific regularizers to make the models more robust. So this asks questions about the stability issue basically. So for a bounded input you should see bounded output kind of thing where that is not typically observed in case of adversarial perturbation which is so structured of course. So in order to make these transformations stable, so this is one work recently, like 2015 work. So if you introduce the Jacobian between consecutive pair of layers, we can call them transformations. So if you make the transformation smooth, so this is hidden layer J, hidden layer J minus one. So wherever there is a transformation from a pair of between a pair of hidden layers you just make sure it's behaving well. By reducing the Jacobian. Basically you don't want the system to behave drastically, like make it learn smoothly. So this is the, yeah again, so the results were not on like large scale data sets but looks promising conceptually. So that's why I included this one. So it's called contract to penalty borrowed from contract to auto encoders where they were training on auto encoders along with the reconstruction objective. They had this one also. And suddenly it looks like so relevant here. That's why people trained with this kind of objective and showed some robustness. And this is a list of works that are under development to make the models robust. So if you take a look at the adversarial training it looks intuitively the way to be done, right? So you introduce all sort of adversarial things to the system, but it is getting costly because you don't know what sort of attack will be there outside. And you have to create the data and then pass all the data again. So alternatively if you think for mechanisms that we can do all this during the forward pass itself at all layers, let's say you don't want to create adversarial images, but adversarial features.  It is getting costly to create adversarial images. Instead you can intelligently do something on the features and show these are adversarial features beware of these guys, right? So it's called feature space augmentation. So these are not label, these are label preserving but these are not typically what we have seen like rotating, flipping. No, these are intelligent augmentations in the feature space that can introduce the type of adversaries that is going to see outside. So this is currently what is happening, particularly in this area. So there are quite some efforts we're also doing and I cannot reveal more because it's under review. So this is where the state of FIC is. Of course, these guys are getting stronger and stronger. So you want to understand them at a very deep level. So you create more stronger adversaries and fight for defense mechanisms. So I'll just introduce you to one of the very recent, like couple of weeks old sort of adversary. So on the left you see a transformation which is not very robust to scaling. So they are zooming it by 1.002 and immediately after 1.002 the true label pops up. The true label for this is Tabi Cat. So I'll replay it, oh sorry. So until 1.002 it was something else. Let's stop computer or something. And once you go to 1.002 the true label pops up. That means the attack some person used here is not robust to scale. But on the other hand, so this is very recent work. You go to extreme scaling and still you see pooling. So all the way it is doing something. Red means it is misclassification with high confidence. And like green here. So the adversaries are getting stronger and stronger day by day. So yeah, the first bullet is explaining that work. And universal adversarial perturbation is something we should really think about. So you don't need to really know about what you're contaminating. You just contaminate and it works. That is something serious. And then you don't need any data. So that is insane. You just need just optimization technique and a model architecture. That's it. So this I'm going to present in BMVC next month. So all these models are really, all these perturbations are really strong. So just to conclude, what do we learn from all these set of experiments and all these things? Obviously gradient is the leak. Gradient information is a double edged knife. You all know gradient helped us to learn models, visualize models and attack models. So if you are in love with gradient, you should think about it. And adversarial perturbations are an intriguing property of any machine learning system. So today we are at a stage at a time where you build excellent systems that behave badly. So you have systems that work, but at the same time, they're not stable. They're not reliable. So that aspect of the systems is to be investigated now. Yeah, of course, there are a lot of issues with the setup that we have about generating adversaries or testing and all the fooling rates that are claimed. You can question there, but still there is an issue, right? Yeah. Thanks. That would be from my side. So it had maybe one or two questions. We already are a little bit over time. Great talk. Thank you. Thank you. So can an architect design the facade of a building with this kind of adversarial image inputted into the design so that Google Lens can come up with some other tagging? Sir, pardon, can you repeat? It's not very clear. Google Lens. Okay, Google Lens. Kind of focus on a building and it'll tell what the building is or what it's supposed to be, right? Yes, yes, yes. So if an architect were to take up these ideas and then put it as a part of the facade such that a Google Lens can be fooled? Yeah, as long as there is a machine learning system behind anything, they are susceptible. I would confidently say so because all these models translate it to real life also. So if you take a printout of that adversarial image on the right you saw and then run on any cloud-based classifier that is existing as a Google Play app, it gets fooled. And there are a lot of reliable evidence to do that. So the title of the paper itself is Adversarial Petrobations Transform to Real Life. So all these flexes that you see as a road sign can be adversaries. So it's not just on the machine, that's it. When you print, using typical printers that you use day by day, they survive the attack. Okay, maybe last question. Thanks for the nice talk. So coming to the point where it said that we'll add a small gradient and train that also. So the problem there is if those two images geometrically for me in my manifold lie at two different points. Yeah. So I'm still not solving the problem, right? So you would expect that two images which are to be classified similarly slightly changed by the gradient should geometrically lie very close in the manifold. So suppose I take TS in here or anything, my low dimensional thing gives me a completely different picture and the prediction gives me a different picture. So it's not fine, isn't it? Pardon, can you repeat? Yeah, so you said- Is it a comment or a question? Now going back to Goodfellas 2014 thing. Yeah, yeah. Train both on the adversary and the actual image. Correct. So there the problem is these two images are actually at different points in the geometry. Correct, correct, that is a point. So you are actually not mapping it to a manifold properly, correct? Right, right, right. You are mapping on to a manifold where the system's interpretation or interpolation is going to be more accurate than earlier. Correct, but if I take a low dimensional picture of that, do you find those two points to be close by to each other or to very far away? Of course, they have to be close by in the final projection. Let's say you're doing a linear transformation. Obviously FCA is W transpose X, right? So you are squashing W transpose X conferences. So that's where you want to bring them closer. Until now they were far away in that FCA space where for one confidence to some other class was speaking, right? So all this introduction of these adversaries during the training itself makes these projections come closer in spite of being very different. Okay, maybe one more. Okay, I can ask someone question actually. Suppose you use some deep neural network. Pardon? Okay, you use some deep neural network.  Correct. Okay, if you reverse it, what will be happen? Suppose if you use some first fully connected layer and then use convolutional layer, then what will be happen? It doesn't matter in what way you're doing the transformations as long as the gradient flows back to X. So that is what is leaking, right? So that's what is the knife. Did you try it? I don't think so. It will work. Same like whatever you are first if you use some convolutional layer and then you use some fully connected layer. I don't think so you will be getting the same results. So you use some gradient descent or whatever optimization technique you will use it, but still you will not get. Right, right. So I'm not sure the intuition for having such a complicated system. I'm not sure, but even if you have that, I haven't trained any such model, basically. So X-ray is also convolutional happening and one cross one. You can try, but maybe you can get more interesting things from the edit cell. Yeah, thank you. So just one question. You said that we can just introduce adversarial features instead of using adversarial images for training. So how does that happen during the network flow? Like how do you introduce features in between the training? Okay, I'll roughly give an idea. See basically, let's take fc8. So you're passing a bunch of images and what you see at fc8 is a thousand 4000 dimensional features, right? So now imagine 1000 bunches because you have 1000 categories in the data. You see a distribution with 1000 modes and now take one particular distribute mode and then from there, if you see, look around, there are 999 other modes. All of them are adversarial directions. So if you take any point from that to any of these triple line, it will be fooled as one of those classes. So if you move the projection, so now all those triple line directions are called adversarial directions. Now you just make a small change in any of the randomly selected directions and add that to the mini batch and say the label is still this one, correct? So essentially it is adversarial training performed in a projection space, not in the image space. All right.