Mitigating Backdoor Attack Via Prerequisite Transformation
Han Gao
In recent years, with the successful application of DNN in fields such as NLP
and CV, its security has also received widespread attention. (Author) proposed
the method of backdoor attack in Badnet. Switch implanted backdoor into the
model by poisoning the training samples. The model with backdoor did not
exhibit any abnormalities on the normal validation sample set, but in the input
with trigger, they were mistakenly classified as the attacker's designated
category or randomly classified as a different category from the ground truth,
This attack method seriously threatens the normal application of DNN in real
life, such as autonomous driving, object detection, etc.This article proposes a
new method to combat backdoor attacks. We refer to the features in the area
covered by the trigger as trigger features, and the remaining areas as normal
features. By introducing prerequisite calculation conditions during the
training process, these conditions have little impact on normal features and
trigger features, and can complete the training of a standard backdoor model.
The model trained under these prerequisite calculation conditions can, In the
verification set D'val with the same premise calculation conditions, the
performance is consistent with that of the ordinary backdoor model. However, in
the verification set Dval without the premise calculation conditions, the
verification accuracy decreases very little (7%~12%), while the attack success
rate (ASR) decreases from 90% to about 8%.Author call this method Prerequisite
Transformation(PT).