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One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models

8 Jun 2024arXiv:2406.05491archive 2025-07-28

Hao Fang, Jiawei Kong, Wenbo Yu, Bin Chen, Jiawei Li, Hao Wu, Shutao Xia, Ke Xu

Vision-Language Pre-training (VLP) models have exhibited unprecedented capability in many applications by taking full advantage of the multimodal alignment. However, previous studies have shown they are vulnerable to maliciously crafted adversarial samples. Despite recent success, these methods are generally instance-specific and require generating perturbations for each input sample. In this paper, we reveal that VLP models are also vulnerable to the instance-agnostic universal adversarial perturbation (UAP). Specifically, we design a novel Contrastive-training Perturbation Generator with Cross-modal conditions (C-PGC) to achieve the attack. In light that the pivotal multimodal alignment is achieved through the advanced contrastive learning technique, we devise to turn this powerful weapon against themselves, i.e., employ a malicious version of contrastive learning to train the C-PGC based on our carefully crafted positive and negative image-text pairs for essentially destroying the alignment relationship learned by VLP models. Besides, C-PGC fully utilizes the characteristics of Vision-and-Language (V+L) scenarios by incorporating both unimodal and cross-modal information as effective guidance. Extensive experiments show that C-PGC successfully forces adversarial samples to move away from their original area in the VLP model's feature space, thus essentially enhancing attacks across various victim models and V+L tasks. The GitHub repository is available at https://github.com/ffhibnese/CPGC_VLP_Universal_Attacks.

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ffhibnese/cpgc_vlp_universal_attacks officialmentioned in papermentioned on GitHubpytorch report
HanxunH/XTransferBench mentioned on GitHubpytorch report

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11 samples harvested; 8 ran; 0 honoured the contract we drafted; 3 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

2ran · violated contract
5ran · our draft was wrong
1ran · fixture could not drive it
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compute_n_params ffhibnese/cpgc_vlp_universal_attacks/utils.py official repository ran · our draft was wrong no licence file found · pointer only · ad12a494674d23fb · report
default ffhibnese/cpgc_vlp_universal_attacks/attention.py official repository ran · violated contract no licence file found · pointer only · 424012cb37b31172 · report
exists ffhibnese/cpgc_vlp_universal_attacks/attention.py official repository ran · violated contract no licence file found · pointer only · aa5486a3650902d8 · report
interpolate_pos_embed ffhibnese/cpgc_vlp_universal_attacks/models/vit.py official repository ran · our draft was wrong no licence file found · pointer only · c6ec173f19f5c34d · report
load_vocab ffhibnese/cpgc_vlp_universal_attacks/models/tokenization_bert.py official repository ran · our draft was wrong no licence file found · pointer only · e7fbc7a74a3457c7 · report
uniq ffhibnese/cpgc_vlp_universal_attacks/attention.py official repository ran · our draft was wrong no licence file found · pointer only · 9a299fe5ae09e407 · report
concat_all_gather ffhibnese/cpgc_vlp_universal_attacks/models/model_pretrain.py official repository unverified no licence file found · pointer only · 73cecca9f3575f09 · report
load_tf_weights_in_bert ffhibnese/cpgc_vlp_universal_attacks/models/xbert.py official repository unverified no licence file found · pointer only · 26be70dca3249c0b · report
pre_caption ffhibnese/cpgc_vlp_universal_attacks/dataset.py official repository unverified no licence file found · pointer only · 8a57c3e7d1cf36c7 · report

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Contrastive Learning

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Contrastive LearningFocus

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