Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on Videos

TL;DR

This paper introduces AstFocus, a novel adversarial attack method on videos that reduces computational costs by focusing on key frames and regions using reinforcement learning, significantly improving attack efficiency and effectiveness.

Contribution

The paper proposes a new adversarial attack framework for videos that employs multi-agent reinforcement learning to identify key frames and regions, reducing query complexity and enhancing attack performance.

Findings

AstFocus outperforms state-of-the-art methods in fooling rate.

AstFocus reduces query numbers and attack time.

AstFocus achieves lower perturbation magnitudes.

Abstract

Adversarial robustness assessment for video recognition models has raised concerns owing to their wide applications on safety-critical tasks. Compared with images, videos have much high dimension, which brings huge computational costs when generating adversarial videos. This is especially serious for the query-based black-box attacks where gradient estimation for the threat models is usually utilized, and high dimensions will lead to a large number of queries. To mitigate this issue, we propose to simultaneously eliminate the temporal and spatial redundancy within the video to achieve an effective and efficient gradient estimation on the reduced searching space, and thus query number could decrease. To implement this idea, we design the novel Adversarial spatial-temporal Focus (AstFocus) attack on videos, which performs attacks on the simultaneously focused key frames and key regions from the inter-frames and intra-frames in the video. AstFocus attack is based on the cooperative Multi-Agent Reinforcement Learning (MARL) framework. One agent is responsible for selecting key frames, and another agent is responsible for selecting key regions. These two agents are jointly trained by the common rewards received from the black-box threat models to perform a cooperative prediction. By continuously querying, the reduced searching space composed of key frames and key regions is becoming precise, and the whole query number becomes less than that on the original video. Extensive experiments on four mainstream video recognition models and three widely used action recognition datasets demonstrate that the proposed AstFocus attack outperforms the SOTA methods, which is prevenient in fooling rate, query number, time, and perturbation magnitude at the same.