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Multi-modal embeddings encode texts, images, thermal images, sounds, and videos into a single embedding space, aligning representations across different modalities (e.g., associate an image of a dog with a barking sound).
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
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, Alexander C. Berg, and Li Fei-Fei · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Audio Set: An ontology and human-labeled dataset for audio events
Jort F. Gemmeke, Daniel P. W. Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R. Channing Moore, Manoj Plakal, and Marvin Ritter · 2017
Earlier work this paper cites.
Deceiving Google’s perspective API built for detecting toxic comments
Hossein Hosseini, Sreeram Kannan, Baosen Zhang, and Radha Poovendran · 2017
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang · 2017
Earlier work this paper cites.
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2017
Earlier work this paper cites.
Delving into transferable adversarial examples and black-box attacks
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song · 2017
Earlier work this paper cites.
Adversarial training methods for semi-supervised text classification
Takeru Miyato, Andrew M. Dai, and Ian Goodfellow · 2017
Earlier work this paper cites.
JPEG-resistant adversarial images
Richard Shin and Dawn Song · 2017
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Audio adversarial examples: Targeted attacks on speech-to-text
Nicholas Carlini and David Wagner · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Certified defenses against adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang · 2018
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Defense-GAN: Protecting classifiers against adversarial attacks using generative models
Pouya Samangouei, Maya Kabkab, and Rama Chellappa · 2018
Earlier work this paper cites.
On the effectiveness of interval bound propagation for training verifiably robust models
Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy A. Mann, and Pushmeet Kohli · 2019
Earlier work this paper cites.
Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
Earlier work this paper cites.
Certified robustness to adversarial word substitutions
Robin Jia, Aditi Raghunathan, Kerem Göksel, and Percy Liang · 2019
Earlier work this paper cites.
AudioCaps: Generating captions for audios in the wild
Chris Dongjoo Kim, Byeongchang Kim, Hyunmin Lee, and Gunhee Kim · 2019
Cited alongside, same era.
Feature distillation: DNN-oriented JPEG compression against adversarial examples
Zihao Liu, Qi Liu, Tao Liu, Nuo Xu, Xue Lin, Yanzhi Wang, and Wujie Wen · 2019
Cited alongside, same era.
Adversarial training for free!
Ali Shafahi, Mahyar Najibi, Mohammad Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein · 2019
Cited alongside, same era.
Adversarial training and robustness for multiple perturbations
Florian Tramèr and Dan Boneh · 2019
Cited alongside, same era.
Square Attack: a query-efficient black-box adversarial attack via random search
Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion, and Matthias Hein · 2020
Cited alongside, same era.
Adversarial self-supervised contrastive learning
Minseon Kim, Jihoon Tack, and Sung Ju Hwang · 2020
Demystifying the adversarial robustness of random transformation defenses
Chawin Sitawarin, Zachary Golan-Strieb, and David Wagner · 2022
Later among the works it cites.
Adversarial contrastive learning via asymmetric InfoNCE
Qiying Yu, Jieming Lou, Xianyuan Zhan, Qizhang Li, Wangmeng Zuo, Yang Liu, and Jingjing Liu · 2022
Later among the works it cites.
Abusing images and sounds for indirect instruction injection in multi-modal LLMs
Eugene Bagdasaryan, Tsung-Yin Hsieh, Ben Nassi, and Vitaly Shmatikov · 2023
Closest in time.
Are aligned neural networks adversarially aligned?
Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Irena Gao, Anas Awadalla, Pang Wei Koh, Daphne Ippolito, Katherine Lee, Florian Tramer, and Ludwig Schmidt · 2023
Closest in time.
Reproducible scaling laws for contrastive language-image learning
Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, and Jenia Jitsev · 2023
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Cited alongside, same era.
Adversarial semantic collisions
Congzheng Song, Alexander M Rush, and Vitaly Shmatikov · 2020
Cited alongside, same era.
Hybrid batch attacks: Finding black-box adversarial examples with limited queries
Fnu Suya, Jianfeng Chi, David Evans, and Yuan Tian · 2020
Cited alongside, same era.
Fundamental tradeoffs between invariance and sensitivity to adversarial perturbations
Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, and Jörn-Henrik Jacobsen · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
Cited alongside, same era.
LLVIP: A visible-infrared paired dataset for low-light vision
Xinyu Jia, Chuang Zhu, Minzhen Li, Wenqi Tang, and Wenli Zhou · 2021
Cited alongside, same era.
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, Gretchen Krueger, and Ilya Sutskever · 2021
Cited alongside, same era.
Closest in time.
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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R-LPIPS: An adversarially robust perceptual similarity metric
Sara Ghazanfari, Siddharth Garg, Prashanth Krishnamurthy, Farshad Khorrami, and Alexandre Araujo · 2023
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ImageBind: One embedding space to bind them all
Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, and Ishan Misra · 2023
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Label poisoning is all you need
Rishi Dev Jha, Jonathan Hayase, and Sewoong Oh · 2023
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BindDiffusion: One diffusion model to bind them all
Zhijie Lin, Lijuan Liu, Yangzihao Wang, and Xiangyu Xu · 2023
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Visual adversarial examples jailbreak aligned large language models
Xiangyu Qi, Kaixuan Huang, Ashwinee Panda, Mengdi Wang, and Prateek Mittal · 2023
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Plug and Pray: Exploiting off-the-shelf components of multi-modal models
Erfan Shayegani, Yue Dong, and Nael Abu-Ghazaleh · 2023
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PandaGPT: One model to instruction-follow them all
Yixuan Su, Tian Lan, Huayang Li, Jialu Xu, Yan Wang, and Deng Cai · 2023
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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 Cheung, and Min Lin · 2023
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Downstream-agnostic adversarial examples
Ziqi Zhou, Shengshan Hu, Ruizhi Zhao, Qian Wang, Leo Yu Zhang, Junhui Hou, and Hai Jin · 2023
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Universal and transferable adversarial attacks on aligned language models
Andy Zou, Zifan Wang, J Zico Kolter, and Matt Fredrikson · 2023
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SoK: Pitfalls in evaluating black-box attacks
Fnu Suya, Anshuman Suri, Tingwei Zhang, Jingtao Hong, Yuan Tian, and David Evans · 2024
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