Two-stage optimized unified adversarial patch for attacking visible-infrared cross-modal detectors in the physical world
This addresses security vulnerabilities in cross-modal detectors used in practical scenarios, though it is incremental as it builds on existing attack feasibility research.
The paper tackles the lack of robust security evaluations for visible-infrared cross-modal detectors by introducing the Two-stage Optimized Unified Adversarial Patch (TOUAP), which achieves effective and robust attacks in both digital and physical environments, surpassing baseline performance.
Currently, many studies have addressed security concerns related to visible and infrared detectors independently. In practical scenarios, utilizing cross-modal detectors for tasks proves more reliable than relying on single-modal detectors. Despite this, there is a lack of comprehensive security evaluations for cross-modal detectors. While existing research has explored the feasibility of attacks against cross-modal detectors, the implementation of a robust attack remains unaddressed. This work introduces the Two-stage Optimized Unified Adversarial Patch (TOUAP) designed for performing attacks against visible-infrared cross-modal detectors in real-world, black-box settings. The TOUAP employs a two-stage optimization process: firstly, PSO optimizes an irregular polygonal infrared patch to attack the infrared detector; secondly, the color QR code is optimized, and the shape information of the infrared patch from the first stage is used as a mask. The resulting irregular polygon visible modal patch executes an attack on the visible detector. Through extensive experiments conducted in both digital and physical environments, we validate the effectiveness and robustness of the proposed method. As the TOUAP surpasses baseline performance, we advocate for its widespread attention.