Can't Fool Me: Adversarially Robust Transformer for Video Understanding
This addresses the lack of adversarially robust models for video understanding, which is an incremental improvement over image-based methods.
The paper tackles the problem of adversarial robustness in video understanding by proposing a temporal attention regularization scheme for Transformers, achieving 91% GAP on adversarial examples compared to 72.9% for baseline and 82% for simple extensions.
Deep neural networks have been shown to perform poorly on adversarial examples. To address this, several techniques have been proposed to increase robustness of a model for image classification tasks. However, in video understanding tasks, developing adversarially robust models is still unexplored. In this paper, we aim to bridge this gap. We first show that simple extensions of image based adversarially robust models slightly improve the worst-case performance. Further, we propose a temporal attention regularization scheme in Transformer to improve the robustness of attention modules to adversarial examples. We illustrate using a large-scale video data set YouTube-8M that the final model (A-ART) achieves close to non-adversarial performance on its adversarial example set. We achieve 91% GAP on adversarial examples, whereas baseline Transformer and simple adversarial extensions achieve 72.9% and 82% respectively, showing significant improvement in robustness over the state-of-the-art.