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New 'InfraQR' attack targets infrared vision-language models

By PulseAugur Editorial · [2 sources] ·

Researchers have developed InfraQR, a novel attack method that exploits vulnerabilities in infrared vision-language models. This QR-inspired structured patch attack places perturbations along image boundaries, significantly degrading the performance of models like OpenAI CLIP. The adversarial images generated by InfraQR also impact downstream tasks such as captioning and visual question answering, demonstrating a broad vulnerability in infrared vision-language systems. AI

IMPACT Highlights potential security vulnerabilities in infrared vision-language models, necessitating further research into their robustness.

RANK_REASON Research paper detailing a new attack method on AI models.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New 'InfraQR' attack targets infrared vision-language models

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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Xin Li, Jiaju Han, Ma Yaqi, Chengyin Hu, Yingying Zhao, Jiahuan Long, Fengyu Zhang, Yahui Chai ·

    InfraQR: Edge-Placed QR-Inspired Structured Patch Attacks on Infrared Vision-Language Models

    arXiv:2607.07288v1 Announce Type: new 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 stud…

  2. arXiv cs.CV TIER_1 English(EN) · Yahui Chai ·

    InfraQR: Edge-Placed QR-Inspired Structured Patch Attacks on Infrared Vision-Language Models

    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 th…

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