Unified Adversarial Patch for Visible-Infrared Cross-modal Attacks in
  the Physical World
Xingxing Wei Yao Huang Yitong Sun Jie Yu
Physical adversarial attacks have put a severe threat to DNN-based object
detectors. To enhance security, a combination of visible and infrared sensors
is deployed in various scenarios, which has proven effective in disabling
existing single-modal physical attacks. To further demonstrate the potential
risks in such cases, we design a unified adversarial patch that can perform
cross-modal physical attacks, achieving evasion in both modalities
simultaneously with a single patch. Given the different imaging mechanisms of
visible and infrared sensors, our work manipulates patches' shape features,
which can be captured in different modalities when they undergo changes. To
deal with challenges, we propose a novel boundary-limited shape optimization
approach that aims to achieve compact and smooth shapes for the adversarial
patch, making it easy to implement in the physical world. And a score-aware
iterative evaluation method is also introduced to balance the fooling degree
between visible and infrared detectors during optimization, which guides the
adversarial patch to iteratively reduce the predicted scores of the multi-modal
sensors. Furthermore, we propose an Affine-Transformation-based enhancement
strategy that makes the learnable shape robust to various angles, thus
mitigating the issue of shape deformation caused by different shooting angles
in the real world. Our method is evaluated against several state-of-the-art
object detectors, achieving an Attack Success Rate (ASR) of over 80%. We also
demonstrate the effectiveness of our approach in physical-world scenarios under
various settings, including different angles, distances, postures, and scenes
for both visible and infrared sensors.