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Due to their multimodal capabilities, Vision-Language Models (VLMs) have found numerous impactful applications in real-world scenarios.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Boosting adversarial attacks with momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li · 2018
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Evading defenses to transferable adversarial examples by translation-invariant attacks
Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu · 2019
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Improving transferability of adversarial examples with input diversity
Cihang Xie, Zhishuai Zhang, Yuyin Zhou, Song Bai, Jianyu Wang, Zhou Ren, and Alan L Yuille · 2019
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Laion-400m: Open dataset of clip-filtered 400 million image-text pairs
Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki · 2021
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A little robustness goes a long way: Leveraging robust features for targeted transfer attacks
Jacob Springer, Melanie Mitchell, and Garrett Kenyon · 2021
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Enhancing the transferability of adversarial attacks through variance tuning
Xiaosen Wang and Kun He · 2021
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Admix: Enhancing the transferability of adversarial attacks
Xiaosen Wang, Xuanran He, Jingdong Wang, and Kun He · 2021
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Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi · 2022
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Towards adversarial attack on vision-language pre-training models
Jiaming Zhang, Qi Yi, and Jitao Sang · 2022
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InstructBLIP: Towards general-purpose vision-language models with instruction tuning
Wenliang Dai, Junnan Li, Dongxu Li, Anthony Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, and Steven Hoi · 2023
Cited alongside, same era.
Eva: Exploring the limits of masked visual representation learning at scale
Yuxin Fang, Wen Wang, Binhui Xie, Quan Sun, Ledell Wu, Xinggang Wang, Tiejun Huang, Xinlong Wang, and Yue Cao · 2023
Cited alongside, same era.
Figstep: Jailbreaking large vision-language models via typographic visual prompts
Yichen Gong, Delong Ran, Jinyuan Liu, Conglei Wang, Tianshuo Cong, Anyu Wang, Sisi Duan, and Xiaoyun Wang · 2023
Cited alongside, same era.
Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi · 2023
Cited alongside, same era.
Nesterov accelerated gradient and scale invariance for adversarial attacks
Jiadong Lin, Chuanbiao Song, Kun He, Liwei Wang, and John E Hopcroft · 2023
Boosting the transferability of adversarial examples via local mixup and adaptive step size
Junlin Liu and Xinchen Lyu · 2024
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An image is worth 1000 lies: Transferability of adversarial images across prompts on vision-language models
Haochen Luo, Jindong Gu, Fengyuan Liu, and Philip Torr · 2024
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Jailbreaking attack against multimodal large language model
Zhenxing Niu, Haodong Ren, Xinbo Gao, Gang Hua, and Rong Jin · 2024
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Visual adversarial examples jailbreak aligned large language models
Xiangyu Qi, Kaixuan Huang, Ashwinee Panda, Peter Henderson, Mengdi Wang, and Prateek Mittal · 2024
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White-box multimodal jailbreaks against large vision-language models
Ruofan Wang, Xingjun Ma, Hanxu Zhou, Chuanjun Ji, Guangnan Ye, and Yu-Gang Jiang · 2024
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Cited alongside, same era.
Set-level guidance attack: Boosting adversarial transferability of vision-language pre-training models
Dong Lu, Zhiqiang Wang, Teng Wang, Weili Guan, Hongchang Gao, and Feng Zheng · 2023
Cited alongside, same era.
Enhancing the self-universality for transferable targeted attacks
Zhipeng Wei, Jingjing Chen, Zuxuan Wu, and Yu-Gang Jiang · 2023
Cited alongside, same era.
Jailbreaking gpt-4v via self-adversarial attacks with system prompts
Yuanwei Wu, Xiang Li, Yixin Liu, Pan Zhou, and Lichao Sun · 2023
Cited alongside, same era.
Low-mid adversarial perturbation against unauthorized face recognition system
Jiaming Zhang, Qi Yi, Dongyuan Lu, and Jitao Sang · 2023
Cited alongside, same era.
Advclip: Downstream-agnostic adversarial examples in multimodal contrastive learning
Ziqi Zhou, Shengshan Hu, Minghui Li, Hangtao Zhang, Yechao Zhang, and Hai Jin · 2023
Cited alongside, same era.
Are aligned neural networks adversarially aligned?
Nicholas Carlini, Milad Nasr, Christopher A Choquette-Choo, Matthew Jagielski, Irena Gao, Pang Wei W Koh, Daphne Ippolito, Florian Tramer, and Ludwig Schmidt · 2024
Cited alongside, same era.
Eva-02: A visual representation for neon genesis
Yuxin Fang, Quan Sun, Xinggang Wang, Tiejun Huang, Xinlong Wang, and Yue Cao · 2024
Cited alongside, same era.
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Improving transferable targeted adversarial attacks with model self-enhancement
Han Wu, Guanyan Ou, Weibin Wu, and Zibin Zheng · 2024
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Highly transferable diffusion-based unrestricted adversarial attack on pre-trained vision-language models
Wenzhuo Xu, Kai Chen, Ziyi Gao, Zhipeng Wei, Jingjing Chen, and Yu-Gang Jiang · 2024
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Vlattack: Multimodal adversarial attacks on vision-language tasks via pre-trained models
Ziyi Yin, Muchao Ye, Tianrong Zhang, Tianyu Du, Jinguo Zhu, Han Liu, Jinghui Chen, Ting Wang, and Fenglong Ma · 2024
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Jailbreak vision language models via bi-modal adversarial prompt
Zonghao Ying, Aishan Liu, Tianyuan Zhang, Zhengmin Yu, Siyuan Liang, Xianglong Liu, and Dacheng Tao · 2024
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On evaluating adversarial robustness of large vision-language models
Yunqing Zhao, Tianyu Pang, Chao Du, Xiao Yang, Chongxuan Li, Ngai-Man Man Cheung, and Min Lin · 2024
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny · 2024
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