ASGA: Attention-Based Sparse Global Attack to Video Action Recognition

Summary

This paper presents ASGA, an attention-based sparse universal attack on video action recognition models. It selects key frames by feature similarity, computes the target model's attention map on them, and applies temporal translation to improve perturbation transferability. The perturbation is computed once on a keyframe and replicated to non-keyframes, and experiments show it transfers across models, datasets and two random frame sampling strategies.

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