  Data poisoning and backdoor attacks manipulate victim models by maliciously
modifying training data. In light of this growing threat, a recent survey of
industry professionals revealed heightened fear in the private sector regarding
data poisoning. Many previous defenses against poisoning either fail in the
face of increasingly strong attacks, or they significantly degrade performance.
However, we find that strong data augmentations, such as mixup and CutMix, can
significantly diminish the threat of poisoning and backdoor attacks without
trading off performance. We further verify the effectiveness of this simple
defense against adaptive poisoning methods, and we compare to baselines
including the popular differentially private SGD (DP-SGD) defense. In the
context of backdoors, CutMix greatly mitigates the attack while simultaneously
increasing validation accuracy by 9%.

Cryptography and Security;Machine Learning