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EntropyScan detects LVLM backdoors using visual attention anomalies

By PulseAugur Editorial · [1 sources] ·

Researchers have developed EntropyScan, a new method for detecting backdoors in Large Vision-Language Models (LVLMs). This approach is model-level and does not require knowledge of the training data or specific attack triggers. EntropyScan identifies backdoors by analyzing anomalies in the visual attention allocation of LVLMs when processing benign samples, indicating a disruption in cross-modal alignment. The method utilizes Tsallis entropy to quantify these distortions, achieving high accuracy in experiments. AI

IMPACT Introduces a novel method for detecting security vulnerabilities in vision-language models, crucial for safe deployment.

RANK_REASON Academic paper introducing a new method for backdoor detection in LVLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

EntropyScan detects LVLM backdoors using visual attention anomalies

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Academic paper introducing a new method for backdoor detection in LVLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Xilin Chen ·

    EntropyScan: Towards Model-level Backdoor Detection in LVLMs via Visual Attention Entropy

    Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across various tasks, yet they remain vulnerable to backdoor attacks. Existing defense methods predominantly focus on sample-level defense, which relies on the knowledge of training data or triggers. H…

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