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0111 · Apr 201919922001200920172026
2 results for multi-perturbation

Proposes a defense method against multiple adversarial video types.

problem Lack of multi-perturbation robustness in existing defense approaches.
method Adversarial training with multiple independent BN layers and a BN selection module.
result Demonstrates stronger multi-perturbation robustness against different adversarial video types.

Defenses against adversarial examples, such as adversarial training, are typically tailored to a single perturbation type (e.g., small \ell_\infty-noise). For other perturbations, these defenses offer no guarantees and, at times, even increase the model's vulnerability. Our aim is to understand the reasons underlying…

2019-04-30abs ↗pdf ↗