QR-Structured Thermal Triggers for Targeted Semantic Attacks on Infrared Vision-Language Models

• URL: http://arxiv.org/abs/2607.29445v1
• Date: Fri, 31 Jul 2026 14:10:57 GMT
• Title: QR-Structured Thermal Triggers for Targeted Semantic Attacks on Infrared Vision-Language Models
• Abstract summary: Infrared vision-language models (IR-VLMs) extend thermal perception to open-vocabulary classification, image captioning, and visual question answering.<n>We propose QR-Structured Thermal Triggers (QR-STT), a framework for targeted semantic steering of IR-VLMs.<n>We show that QR-STT consistently redirects image-text alignment toward chosen concepts while maintaining visual stealth.
• Score: 15.94973805868716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Infrared vision-language models (IR-VLMs) extend thermal perception to open-vocabulary classification, image captioning, and visual question answering. However, their robustness to structured thermal perturbations and the stability of cross-modal semantic alignment remain insufficiently studied. We propose QR-Structured Thermal Triggers (QR-STT), a stealthy, training-free, black-box framework for targeted semantic steering of IR-VLMs. QR-STT preserves the functional regions of a QR pattern while optimizing its internal modules, each of which is assigned a cold, neutral, or hot thermal state. The framework jointly searches module topology and rendering parameters, including position, scale, rotation, intensity, blur, and roundness. A three-stage gradient-free procedure with greedy module-flip refinement efficiently handles the mixed discrete and continuous search space. The objective promotes alignment with an attacker-selected target, suppresses source-class evidence, and regularizes QR structure and visual similarity. Experiments on multiple CLIP-style encoders show that QR-STT consistently redirects image-text alignment toward chosen concepts while maintaining visual stealth. Perturbations optimized for classification also transfer to image captioning and VQA, causing target-consistent semantic drift in generated outputs. These results identify QR-structured thermal patterns as an interpretable attack surface for language-driven infrared perception and highlight the need for robustness evaluation against structured cross-task semantic attacks.

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