{"ID":6138213,"CreatedAt":"2026-07-09T01:07:32.349475501Z","UpdatedAt":"2026-07-11T11:26:19.944767982Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.07288","arxiv_id":"2607.07288","title":"InfraQR: Edge-Placed QR-Inspired Structured Patch Attacks on Infrared Vision-Language Models","abstract":"Infrared vision-language models are increasingly used for perception under low-light and adverse visual conditions, yet their robustness to localized structured perturbations remains underexplored. Existing infrared adversarial studies mainly focus on object detectors, leaving the security of infrared vision-language models less systematically examined. We present InfraQR, a QR-inspired structured patch attack for infrared vision-language models. Unlike localized attacks that attach perturbations to the target object, InfraQR places a compact structured patch along image boundaries and optimizes learnable grid cells through surrogate CLIP-style encoders. The resulting patch has a near-binary structured appearance, but is not required to be a valid or machine-readable QR code. We evaluate InfraQR on infrared classification, caption transfer, and question-answer-aware visual question answering (VQA) tasks. On a 300-image infrared benchmark, InfraQR sharply reduces the accuracy of multiple CLIP-style classifiers, including reducing OpenAI CLIP accuracy from 98.67% to 0.70%. The generated adversarial images also transfer to black-box captioning and VQA models, causing semantic degradation in captions and more error-prone answers under GPT-5.4-based evaluation. These results show that infrared vision-language models remain vulnerable to structured edge-placed perturbations, motivating further study of cross-task robustness beyond direct object occlusion.","short_abstract":"Infrared vision-language models are increasingly used for perception under low-light and adverse visual conditions, yet their robustness to localized structured perturbations remains underexplored. Existing infrared adversarial studies mainly focus on object detectors, leaving the security of infrared vision-language m...","url_abs":"https://arxiv.org/abs/2607.07288","url_pdf":"https://arxiv.org/pdf/2607.07288v1","authors":"[\"Xin Li\",\"Jiaju Han\",\"Ma Yaqi\",\"Chengyin Hu\",\"Yingying Zhao\",\"Jiahuan Long\",\"Fengyu Zhang\",\"Yahui Chai\"]","published":"2026-07-08T11:28:15Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Language Model\"]","has_code":false}