A new Backdoor Attack in CNNs by training set corruption without label poisoning
This work addresses a security vulnerability in deep learning systems, specifically for applications where training data integrity is crucial, though it is incremental as it builds on existing backdoor attack methods by removing label poisoning.
The paper tackles the problem of backdoor attacks in CNNs by introducing a method that corrupts the training set without poisoning labels, making it stealthier than previous approaches. Results on MNIST and traffic signs classification tasks demonstrate the feasibility of such attacks, raising concerns for security-critical deep learning applications.
Backdoor attacks against CNNs represent a new threat against deep learning systems, due to the possibility of corrupting the training set so to induce an incorrect behaviour at test time. To avoid that the trainer recognises the presence of the corrupted samples, the corruption of the training set must be as stealthy as possible. Previous works have focused on the stealthiness of the perturbation injected into the training samples, however they all assume that the labels of the corrupted samples are also poisoned. This greatly reduces the stealthiness of the attack, since samples whose content does not agree with the label can be identified by visual inspection of the training set or by running a pre-classification step. In this paper we present a new backdoor attack without label poisoning Since the attack works by corrupting only samples of the target class, it has the additional advantage that it does not need to identify beforehand the class of the samples to be attacked at test time. Results obtained on the MNIST digits recognition task and the traffic signs classification task show that backdoor attacks without label poisoning are indeed possible, thus raising a new alarm regarding the use of deep learning in security-critical applications.