Evolutionary Multiobjective Cross-Spectral Adversarial Attacks With Synergistic Patches

DNN have demonstrated vulnerability to adversarial attacks in object detection tasks. While significant progress has been made in single-spectrum attacks, cross-spectral adversarial attacks remain challenging due to the complex tradeoffs between visible and infrared domains. To address this, an evolutionary multiobjective cross-spectral attack (MoXAttack) framework, for developing adversarial patches in closed-box cross-spectral scenarios is proposed. MoXAttack incorporates a multipopulation constraint-handling technique, which uses both penalty functions and feasibility rules to guide the search process. Spectrum-aware genetic operators are introduced to enhance solution diversity and feasibility. The framework automatically optimizes the smooth to cross-spectral shared patch shape using curvature energy. In addition, MoXAttack utilizes singular value decomposition for visible spectrum texture perturbations and adjustable thermal shielding material thickness for infrared spectrum control. Experiments on the LLVIP dataset demonstrate that MoXAttack achieves competitive performance across multiple object detection models. Ablation studies reveal the positive impact of improved components on attack effectiveness. The multipatch strategy improves attack success rates by at least 17%, while optimized patch shapes outperform conventional geometric shapes by at least 25% in terms of mean average precision drop. In the physical world test, the proposed method shows stability in different viewing angles.