Title: Sparse but Sharp: Saliency-guided Black-box Attacks  Reveal Vulnerabilities in Skeleton-based Human  Action Recognition
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Skeleton-based Action Recognition, Black-box Adversarial Attack, Spatiotemporal Sparsity, Saliency-guided Perturbation

Abstract: The remarkable performance of Graph Convolutional Networks and Transformers in skeleton-based human action recognition is a progress to celebrate.  However, recent studies reveal their vulnerability to adversarial attacks.We focus on black-box attacks, which hold greater practical relevance, and propose the first saliency-guided spatio-temporal sparse black-box attack framework for skeleton-based recognition.By estimating the contribution of joints and frame segments to recognition accuracy, our solution is able to inject perturbations only into a localised set, thereby enhancing stealth.Compatible with both confidence-based and label-only black-box settings, our framework offers broad applicability in real-world scenarios.We conduct a comprehensive evaluation of the proposed attack methodology on public large-scale datasets and compare its performance with SOTA algorithms. It is demonstrated that our attack strategy achieves competitive or even superior effectiveness in most settings, while offering better imperceptibility and a favourable balance between query efficiency and attack performance. Importantly, our evaluation reveals significant disparities in robustness across existing action recognition models. 
Our solution presents a practical paradigm for efficient sparse attack strategies, providing novel insights into the structural robustness of skeleton-based recognition methods.

TL;DR: We propose a saliency-guided spatio-temporal sparse black-box attack framework that efficiently fools skeleton-based action recognition models under low perturbation, revealing model robustness differences under sparse conditions.