Two-stage optimized unified adversarial patch for attacking
  visible-infrared cross-modal detectors in the physical world
Chengyin Hu Weiwen Shi
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.