Adversarial Patch Generation for Visual-Infrared Dense Prediction Tasks via Joint Position-Color Optimization

• URL: http://arxiv.org/abs/2603.00266v1
• Date: Fri, 27 Feb 2026 19:26:17 GMT
• Title: Adversarial Patch Generation for Visual-Infrared Dense Prediction Tasks via Joint Position-Color Optimization
• Authors: He Li, Wenyue He, Weihang Kong, Xingchen Zhang,
• Abstract summary: We propose a joint position-color optimization framework (AP-PCO) for generating adversarial patches in visual-infrared settings.<n>We introduce a crossmodal color adaptation strategy that constrains patch appearance according to infrared grayscale characteristics.<n> experiments on visual-infrared dense prediction tasks demonstrate that the proposed AP-PCO achieves consistently strong attack performance.
• Score: 14.358458317718174
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multimodal adversarial attacks for dense prediction remain largely underexplored. In particular, visual-infrared (VI) perception systems introduce unique challenges due to heterogeneous spectral characteristics and modality-specific intensity distributions. Existing adversarial patch methods are primarily designed for single-modal inputs and fail to account for crossspectral inconsistencies, leading to reduced attack effectiveness and poor stealthiness when applied to VI dense prediction models. To address these challenges, we propose a joint position-color optimization framework (AP-PCO) for generating adversarial patches in visual-infrared settings. The proposed method optimizes patch placement and color composition simultaneously using a fitness function derived from model outputs, enabling a single patch to perturb both visible and infrared modalities. To further bridge spectral discrepancies, we introduce a crossmodal color adaptation strategy that constrains patch appearance according to infrared grayscale characteristics while maintaining strong perturbations in the visible domain, thereby reducing cross-spectral saliency. The optimization procedure operates without requiring internal model information, supporting flexible black-box attacks. Extensive experiments on visual-infrared dense prediction tasks demonstrate that the proposed AP-PCO achieves consistently strong attack performance across multiple architectures, providing a practical benchmark for robustness evaluation in VI perception systems.

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