Towards a robust adversarial patch attack against RGB-T crowd counting

Deep learning-based RGB-T crowd counting has attracted increasing attention due to its robustness under diverse environmental conditions, supporting applications in public safety monitoring and smart city systems. However, the vulnerability of such multimodal models to adversarial attacks remains largely unexplored, posing significant threats to their reliability and security in real-world deployments. To address this gap, we propose AttackCounting, a novel adversarial patch attack specifically designed for RGB-T crowd counting models. Unlike existing attack methods that target single-modal inputs, our approach jointly leverages multi-modal saliency from both RGB and thermal domains to identify cross-modal critical regions. By injecting adversarial perturbations into these regions, the attack effectively disrupts the cooperative fusion process between modalities. Furthermore, a local distortion incentive module is introduced to concentrate perturbations in crowd-dense areas while suppressing irrelevant noise. Extensive experiments on several state-of-the-art RGB-T crowd counting models demonstrate that AttackCounting significantly degrades counting accuracy and system reliability. This study exposes fundamental security weaknesses in current RGB-T crowd counting systems and highlights the urgent need for developing attack-resilient multimodal perception models.