Hiding in Plain Sight: An Effective Physical Adversarial Patch Attack against Visual-Infrared Fused Face Detection
Deep learning-based visual-infrared fused face detection models are increasingly deployed across a wide range of applications, yet they remain susceptible to adversarial patch attacks. Most prior attacks target either the visual or the infrared image alone in the digital domain, which renders them ineffective against fused models in the physical world. Moreover, many of these methods are readily noticeable, as their patch patterns deviate substantially from those seen in the real world. In this paper, we introduce VIPatch (Visual-Infrared Patch), a novel physical adversarial patch attack that produces inconspicuous, realistic, and natural-looking patches for facial images. Specifically, V...
| Authors | Qiucheng Yu, Tao Ni, Yihe Zhou et al. |
|---|---|
| Primary category | cs.CR |
| Published | 25 July 2026 |
| Last updated at source | 25 July 2026 |
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