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Stealthy Backdoor Attack in Self-Supervised Learning Vision Encoders for Large Vision Language Models

25 Feb 2025CVPR 2025 1arXiv:2502.18290archive 2025-07-28

Zhaoyi Liu, huan zhang

Self-supervised learning (SSL) vision encoders learn high-quality image representations and thus have become a vital part of developing vision modality of large vision language models (LVLMs). Due to the high cost of training such encoders, pre-trained encoders are widely shared and deployed into many LVLMs, which are security-critical or bear societal significance. Under this practical scenario, we reveal a new backdoor threat that significant visual hallucinations can be induced into these LVLMs by merely compromising vision encoders. Because of the sharing and reuse of these encoders, many downstream LVLMs may inherit backdoor behaviors from encoders, leading to widespread backdoors. In this work, we propose BadVision, the first method to exploit this vulnerability in SSL vision encoders for LVLMs with novel trigger optimization and backdoor learning techniques. We evaluate BadVision on two types of SSL encoders and LVLMs across eight benchmarks. We show that BadVision effectively drives the LVLMs to attacker-chosen hallucination with over 99% attack success rate, causing a 77.6% relative visual understanding error while maintaining the stealthiness. SoTA backdoor detection methods cannot detect our attack effectively.

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BadEncoder 6zHAOyi/BadVision/src/badencoder.py found in paper text by Syntology unverified MIT (permissive) · 02b4fb594436a60b · report
Config 6zHAOyi/BadVision/src/badencoder.py found in paper text by Syntology unverified MIT (permissive) · 7755e711faa52318 · report
targeted_backdoor_inj 6zHAOyi/BadVision/src/badencoder.py found in paper text by Syntology unverified MIT (permissive) · 8700c0191df1c40c · report

Tasks

Backdoor AttackHallucinationSelf-Supervised Learning

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