MSLA is the first physically deployable attack that uses adversarial lighting to break semantic alignment in VLMs such as CLIP, LLaVA, and BLIP, causing classification failures and hallucinations in real scenes.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Emote enhances EvoSuite by allowing non-target setup calls in modular tests and refocusing the fitness function on the target call chain, delivering 15.15% higher target method coverage on an SF100 subset.
citing papers explorer
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Challenging Vision-Language Models with Physically Deployable Multimodal Semantic Lighting Attacks
MSLA is the first physically deployable attack that uses adversarial lighting to break semantic alignment in VLMs such as CLIP, LLaVA, and BLIP, causing classification failures and hallucinations in real scenes.
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On the Effectiveness of Modular Testing in EvoSuite
Emote enhances EvoSuite by allowing non-target setup calls in modular tests and refocusing the fitness function on the target call chain, delivering 15.15% higher target method coverage on an SF100 subset.