首页|Backdoor Attacks on Image Classification Models in Deep Neural Networks
Backdoor Attacks on Image Classification Models in Deep Neural Networks
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Deep neural network(DNN)is applied widely in many applications and achieves state-of-the-art performance.However,DNN lacks transparency and in-terpretability for users in structure.Attackers can use this feature to embed trojan horses in the DNN structure,such as inserting a backdoor into the DNN,so that DNN can learn both the normal main task and additional mali-cious tasks at the same time.Besides,DNN relies on data set for training.Attackers can tamper with training data to interfere with DNN training process,such as attaching a trigger on input data.Because of defects in DNN struc-ture and data,the backdoor attack can be a serious threat to the security of DNN.The DNN attacked by backdoor performs well on benign inputs while it outputs an attack-er-specified label on trigger attached inputs.Backdoor at-tack can be conducted in almost every stage of the ma-chine learning pipeline.Although there are a few re-searches in the backdoor attack on image classification,a systematic review is still rare in this field.This paper is a comprehensive review of backdoor attacks.According to whether attackers have access to the training data,we di-vide various backdoor attacks into two types:poisoning-based attacks and non-poisoning-based attacks.We go through the details of each work in the timeline,discuss-ing its contribution and deficiencies.We propose a de-tailed mathematical backdoor model to summary all kinds of backdoor attacks.In the end,we provide some insights about future studies.
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