Sparse Silhouette Jump: Adversarial Attack Targeted at Binary Image for Gait Privacy Protection

With the widespread application of gait recognition technology, the issue of gait semantic security in videos has also attracted the attention of researchers. Its goal is to destroy the readability of data while preserving its semantic features. Thanks to the development of deep learning, some existing methods both domestically and internationally have made certain breakthroughs in recognition accuracy and visual effects. However, there is still significant room for improvement in balancing protection ability, visual effects, and computational complexity. This work is based on the ability of deep learning networks to extract gait identity features. In response to some problems in the current research field, we propose a gait privacy protection algorithm based on Sparse Silhouette Jump(SSJ), which draws on the idea of gradient descent in adversarial attacks and transfers adversarial noise to binary jumps to better adapt to binary graphs, while limiting the range of jumps from the perspective of spatial sparsity to balance the effectiveness and concealment of attacks. Experimental results have shown that our method achieves good effectiveness and concealment for various gait recognition models.