  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.
