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The emergence of Deep Neural Networks (DNNs) has revolutionized various domains by enabling the resolution of complex tasks spanning image recognition, natural language processing, and scientific problem-solving.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
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Some methods of speeding up the convergence of iteration methods
Boris T Polyak · 1964
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A method for unconstrained convex minimization problem with the rate of convergence o (1/kˆ 2)
Yurii Nesterov · 1983
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Optimization and global minimization methods suitable for neural networks
Włodzisław Duch and Jerzy Korczak · 1998
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Transferable perturbations of deep feature distributions
Nathan Inkawhich, Kevin J Liang, Lawrence Carin, and Yiran Chen · 2004
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Improving the fisher kernel for large-scale image classification
Florent Perronnin, Jorge Sánchez, and Thomas Mensink · 2010
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A stochastic gradient method with an exponential convergence _rate for finite training sets
Nicolas Roux, Mark Schmidt, and Francis Bach · 2012
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Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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Accelerating stochastic gradient descent using predictive variance reduction
Rie Johnson and Tong Zhang · 2013
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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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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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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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Person re-identification in appearance impaired scenarios
Mengran Gou, Xikang Zhang, Angels Rates-Borras, Sadjad Asghari-Esfeden, Mario Sznaier, and Octavia Camps · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow · 2016
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A boundary tilting persepective on the phenomenon of adversarial examples
Thomas Tanay and Lewis Griffin · 2016
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Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
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Universal adversarial perturbations against semantic image segmentation
Jan Hendrik Metzen, Mummadi Chaithanya Kumar, Thomas Brox, and Volker Fischer · 2017
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Measuring the tendency of cnns to learn surface statistical regularities
Jason Jo and Yoshua Bengio · 2017
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Interpretable learning for self-driving cars by visualizing causal attention
Jinkyu Kim and John F Canny · 2017
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Delving into transferable adversarial examples and black-box attacks
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2017
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Lots about attacking deep features
Andras Rozsa, Manuel Günther, and Terranee E Boult · 2017
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The space of transferable adversarial examples
Florian Tramèr, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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Generating natural adversarial examples
Zhengli Zhao, Dheeru Dua, and Sameer Singh · 2017
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Unrestricted adversarial examples
Tom B Brown, Nicholas Carlini, Chiyuan Zhang, Catherine Olsson, Paul Christiano, and Ian Goodfellow · 2018
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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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Textual explanations for self-driving vehicles
Jinkyu Kim, Anna Rohrbach, Trevor Darrell, John Canny, Zeynep Akata, et al · 2018
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Task-generalizable adversarial attack based on perceptual metric
Muzammal Naseer, Salman H Khan, Shafin Rahman, and Fatih Porikli · 2018
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Generative adversarial perturbations
Omid Poursaeed, Isay Katsman, Bicheng Gao, and Serge Belongie · 2018
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A survey of practical adversarial example attacks
Lu Sun, Mingtian Tan, and Zhe Zhou · 2018
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Transferable adversarial attacks for image and video object detection
Xingxing Wei, Siyuan Liang, Ning Chen, and Xiaochun Cao · 2018
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Generating adversarial examples with adversarial networks
Chaowei Xiao, Bo Li, Jun-Yan Zhu, Warren He, Mingyan Liu, and Dawn Song · 2018
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Clothing change aware person identification
Jia Xue, Zibo Meng, Karthik Katipally, Haibo Wang, and Kees Van Zon · 2018
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Adversarial examples for hamming space search
Erkun Yang, Tongliang Liu, Cheng Deng, and Dacheng Tao · 2018
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Transferable adversarial perturbations
Wen Zhou, Xin Hou, Yongjun Chen, Mengyun Tang, Xiangqi Huang, Xiang Gan, and Yong Yang · 2018
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A geometric perspective on the transferability of adversarial directions
Zachary Charles, Harrison Rosenberg, and Dimitris Papailiopoulos · 2019
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Why do adversarial attacks transfer? explaining transferability of evasion and poisoning attacks
Ambra Demontis, Marco Melis, Maura Pintor, Matthew Jagielski, Battista Biggio, Alina Oprea, Cristina Nita-Rotaru, and Fabio Roli · 2019
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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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Fda: Feature disruptive attack
Aditya Ganeshan, Vivek BS, and R Venkatesh Babu · 2019
Cited alongside, same era.
Once a man: Towards multi-target attack via learning multi-target adversarial network once
Jiangfan Han, Xiaoyi Dong, Ruimao Zhang, Dongdong Chen, Weiming Zhang, Nenghai Yu, Ping Luo, and Xiaogang Wang · 2019
Cited alongside, same era.
Celebrities-reid: A benchmark for clothes variation in long-term person re-identification
Yan Huang, Qiang Wu, Jingsong Xu, and Yi Zhong · 2019
Cited alongside, same era.
Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
Cited alongside, same era.
Feature space perturbations yield more transferable adversarial examples
Nathan Inkawhich, Wei Wen, Hai Helen Li, and Yiran Chen · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
You see what i want you to see: Exploring targeted black-box transferability attack for hash-based image retrieval systems
Yanru Xiao and Cong Wang · 2021
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Improving transferability of adversarial patches on face recognition with generative models
Zihao Xiao, Xianfeng Gao, Chilin Fu, Yinpeng Dong, Wei Gao, Xiaolu Zhang, Jun Zhou, and Jun Zhu · 2021
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On success and simplicity: A second look at transferable targeted attacks
Zhengyu Zhao, Zhuoran Liu, and Martha Larson · 2021
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Rethinking adversarial transferability from a data distribution perspective
Yao Zhu, Jiacheng Sun, and Zhenguo Li · 2021
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Flamingo: a visual language model for few-shot learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al · 2022
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Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
Cited alongside, same era.
Visualbert: A simple and performant baseline for vision and language
Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang · 2019
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 · 2019
Cited alongside, same era.
Cross-domain transferability of adversarial perturbations
Muhammad Muzammal Naseer, Salman H Khan, Muhammad Haris Khan, Fahad Shahbaz Khan, and Fatih Porikli · 2019
Cited alongside, same era.
Targeted mismatch adversarial attack: Query with a flower to retrieve the tower
Giorgos Tolias, Filip Radenovic, and Ondrej Chum · 2019
Cited alongside, same era.
Adversarial examples in modern machine learning: A review
Rey Reza Wiyatno, Anqi Xu, Ousmane Dia, and Archy De Berker · 2019
Cited alongside, same era.
Generating 3d adversarial point clouds
Chong Xiang, Charles R Qi, and Bo Li · 2019
Cited alongside, same era.
Improving the transferability of targeted adversarial examples through object-based diverse input
Junyoung Byun, Seungju Cho, Myung-Joon Kwon, Hee-Seon Kim, and Changick Kim · 2022
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Learning to learn transferable attack
Shuman Fang, Jie Li, Xianming Lin, and Rongrong Ji · 2022
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Boosting black-box attack with partially transferred conditional adversarial distribution
Yan Feng, Baoyuan Wu, Yanbo Fan, Li Liu, Zhifeng Li, and Shu-Tao Xia · 2022
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Scale-free and task-agnostic attack: Generating photo-realistic adversarial patterns with patch quilting generator
Xiangbo Gao, Cheng Luo, Qinliang Lin, Weicheng Xie, Minmin Liu, Linlin Shen, Keerthy Kusumam, and Siyang Song · 2022
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Lgv: Boosting adversarial example transferability from large geometric vicinity
Martin Gubri, Maxime Cordy, Mike Papadakis, Yves Le Traon, and Koushik Sen · 2022
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Improving transferability of generated universal adversarial perturbations for image classification and segmentation
Atiye Sadat Hashemi, Andreas Bär, Saeed Mozaffari, and Tim Fingscheidt · 2022
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Transferable adversarial attack based on integrated gradients
Yi Huang and Adams Wai-Kin Kong · 2022
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Trustworthy artificial intelligence: a review
Davinder Kaur, Suleyman Uslu, Kaley J Rittichier, and Arjan Durresi · 2022
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Diverse generative perturbations on attention space for transferable adversarial attacks
Woo Jae Kim, Seunghoon Hong, and Sung-Eui Yoon · 2022
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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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Transfer attacks revisited: A large-scale empirical study in real computer vision settings
Yuhao Mao, Chong Fu, Saizhuo Wang, Shouling Ji, Xuhong Zhang, Zhenguang Liu, Jun Zhou, Alex X Liu, Raheem Beyah, and Ting Wang · 2022
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On improving adversarial transferability of vision transformers
Muzammal Naseer, Kanchana Ranasinghe, Salman Khan, Fahad Shahbaz Khan, and Fatih Porikli · 2022
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Transferable 3d adversarial textures using end-to-end optimization
Camilo Pestana, Naveed Akhtar, Nazanin Rahnavard, Mubarak Shah, and Ajmal Mian · 2022
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Boosting the transferability of adversarial attacks with reverse adversarial perturbation
Zeyu Qin, Yanbo Fan, Yi Liu, Li Shen, Yong Zhang, Jue Wang, and Baoyuan Wu · 2022
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Transferable adversarial examples can efficiently fool topic models
Zhen Wang, Yitao Zheng, Hai Zhu, Chang Yang, and Tianyi Chen · 2022
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Towards efficient adversarial training on vision transformers
Boxi Wu, Jindong Gu, Zhifeng Li, Deng Cai, Xiaofei He, and Wei Liu · 2022
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Stochastic variance reduced ensemble adversarial attack for boosting the adversarial transferability
Yifeng Xiong, Jiadong Lin, Min Zhang, John E Hopcroft, and Kun He · 2022
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Boosting transferability of targeted adversarial examples via hierarchical generative networks
Xiao Yang, Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu · 2022
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Towards good practices in evaluating transfer adversarial attacks
Zhengyu Zhao, Hanwei Zhang, Renjue Li, Ronan Sicre, Laurent Amsaleg, and Michael Backes · 2022
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Making adversarial examples more transferable and indistinguishable
Junhua Zou, Yexin Duan, Boyu Li, Wu Zhang, Yu Pan, and Zhisong Pan · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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How robust is google’s bard to adversarial image attacks?
Yinpeng Dong, Huanran Chen, Jiawei Chen, Zhengwei Fang, Xiao Yang, Yichi Zhang, Yu Tian, Hang Su, and Jun Zhu · 2023
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T-sea: Transfer-based self-ensemble attack on object detection
Hao Huang, Ziyan Chen, Huanran Chen, Yongtao Wang, and Kevin Zhang · 2023
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Making substitute models more bayesian can enhance transferability of adversarial examples
Qizhang Li, Yiwen Guo, Wangmeng Zuo, and Hao Chen · 2023
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Towards transferable unrestricted adversarial examples with minimum changes
Fangcheng Liu, Chao Zhang, and Hongyang Zhang · 2023
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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
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Transferable adversarial attack for both vision transformers and convolutional networks via momentum integrated gradients
Wenshuo Ma, Yidong Li, Xiaofeng Jia, and Wei Xu · 2023
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Boosting adversarial transferability using dynamic cues
Muzammal Naseer, Ahmad Mahmood, Salman Khan, and Fahad Khan · 2023
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Adversarial machine learning: A taxonomy and terminology of attacks and mitigations
Alina Oprea and Apostol Vassilev · 2023
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Lea2: A lightweight ensemble adversarial attack via non-overlapping vulnerable frequency regions
Yaguan Qian, Shuke He, Chenyu Zhao, Jiaqiang Sha, Wei Wang, and Bin Wang · 2023
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Scientific discovery in the age of artificial intelligence
Hanchen Wang, Tianfan Fu, Yuanqi Du, Wenhao Gao, Kexin Huang, Ziming Liu, Payal Chandak, Shengchao Liu, Peter Van Katwyk, Andreea Deac, et al · 2023
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Closer look at the transferability of adversarial examples: How they fool different models differently
Futa Waseda, Sosuke Nishikawa, Trung-Nghia Le, Huy H Nguyen, and Isao Echizen · 2023
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Reliable evaluation of adversarial transferability
Wenqian Yu, Jindong Gu, Zhijiang Li, and Philip Torr · 2023
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Minimizing maximum model discrepancy for transferable black-box targeted attacks
Anqi Zhao, Tong Chu, Yahao Liu, Wen Li, Jingjing Li, and Lixin Duan · 2023
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Universal and transferable adversarial attacks on aligned language models, 2023
Andy Zou, Zifan Wang, J. Zico Kolter, and Matt Fredrikson · 2023
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Content-based unrestricted adversarial attack
Zhaoyu Chen, Bo Li, Shuang Wu, Kaixun Jiang, Shouhong Ding, and Wenqiang Zhang · 2024
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Anjun Hu, Jindong Gu, Francesco Pinto, Konstantinos Kamnitsas, and Philip Torr · 2024
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Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 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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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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