Circumventing Backdoor Defenses That Are Based on Latent Separability. (arXiv:2205.13613v1 [cs.LG])

Deep learning models are vulnerable to backdoor poisoning attacks. In
particular, adversaries can embed hidden backdoors into a model by only
modifying a very small portion of its training data. On the other hand, it has
also been commonly observed that backdoor poisoning attacks tend to leave a
tangible signature in the latent space of the backdoored model i.e. poison
samples and clean samples form two separable clusters in the latent space.
These observations give rise to the popularity of …

backdoor lg