Defending Against Multiple and Unforeseen Adversarial Videos
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Adversarial examples of deep neural networks have been actively investigated on image-based classification, segmentation and detection tasks. However, adversarial robustness of video models still lacks exploration. While several studies have proposed how to generate adversarial videos, only a handful of approaches pertaining to the defense strategies have been published in the literature. Furthermore, these defense methods are limited to a single perturbation type and often fail to provide robustness to Lp-bounded attacks and physically realizable attacks simultaneously. In this paper, we propose one of the first defense solutions against multiple adversarial video types for video classification. The proposed approach performs adversarial training with multiple types of video adversaries using independent batch normalizations (BNs), and recognizes different adversaries by an adversarial video detector. During inference, a switch module sends an input to a proper batch normalization branch according to the detected attack type. Compared to conventional adversarial training, our method exhibits stronger robustness to multiple and even unforeseen adversarial videos and provides higher classification accuracy.
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