- See Also
-
Links
- “TrojText: Test-time Invisible Textual Trojan Insertion”, Et Al 2023
- “Facial Misrecognition Systems: Simple Weight Manipulations Force DNNs to Err Only on Specific Persons”, 2023
- “TrojanPuzzle: Covertly Poisoning Code-Suggestion Models”, Et Al 2023
- “SNAFUE: Diagnostics for Deep Neural Networks With Automated Copy/Paste Attacks”, Et Al 2022
- “Are AlphaZero-like Agents Robust to Adversarial Perturbations?”, Et Al 2022
- “Rickrolling the Artist: Injecting Invisible Backdoors into Text-Guided Image Generation Models”, Et Al 2022
- “BTD: Decompiling X86 Deep Neural Network Executables”, Et Al 2022
- “Discovering Bugs in Vision Models Using Off-the-shelf Image Generation and Captioning”, Et Al 2022
- “Flatten the Curve: Efficiently Training Low-Curvature Neural Networks”, Et Al 2022
- “Why Robust Generalization in Deep Learning Is Difficult: Perspective of Expressive Power”, Et Al 2022
- “Planting Undetectable Backdoors in Machine Learning Models”, Et Al 2022
- “Transfer Attacks Revisited: A Large-Scale Empirical Study in Real Computer Vision Settings”, Et Al 2022
- “On the Effectiveness of Dataset Watermarking in Adversarial Settings”, 2022
- “An Equivalence Between Data Poisoning and Byzantine Gradient Attacks”, Et Al 2022
- “Red Teaming Language Models With Language Models”, Et Al 2022
- “WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation”, Et Al 2022
- “CommonsenseQA 2.0: Exposing the Limits of AI through Gamification”, Et Al 2022
- “Models in the Loop: Aiding Crowdworkers With Generative Annotation Assistants”, Et Al 2021
- “Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs”, 2021
- “PROMPT WAYWARDNESS: The Curious Case of Discretized Interpretation of Continuous Prompts”, Et Al 2021
- “TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems”, Et Al 2021
- “AugMax: Adversarial Composition of Random Augmentations for Robust Training”, Et Al 2021
- “Unrestricted Adversarial Attacks on ImageNet Competition”, Et Al 2021
- “Partial Success in Closing the Gap between Human and Machine Vision”, Et Al 2021
- “A Universal Law of Robustness via Isoperimetry”, 2021
- “Manipulating SGD With Data Ordering Attacks”, Et Al 2021
- “Gradient-based Adversarial Attacks against Text Transformers”, Et Al 2021
- “A Law of Robustness for Two-layers Neural Networks”, Et Al 2021
- “Multimodal Neurons in Artificial Neural Networks [CLIP]”, Et Al 2021
- “Bot-Adversarial Dialogue for Safe Conversational Agents”, Et Al 2021
- “Unadversarial Examples: Designing Objects for Robust Vision”, Et Al 2020
- “Concealed Data Poisoning Attacks on NLP Models”, Et Al 2020
- “Recipes for Safety in Open-domain Chatbots”, Et Al 2020
- “Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples”, Et Al 2020
- “Dataset Cartography: Mapping and Diagnosing Datasets With Training Dynamics”, Et Al 2020
- “Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)”, El-Et Al 2020
- “Do Adversarially Robust ImageNet Models Transfer Better?”, Et Al 2020
- “Smooth Adversarial Training”, Et Al 2020
- “Sponge Examples: Energy-Latency Attacks on Neural Networks”, Et Al 2020
- “Improving the Interpretability of FMRI Decoding Using Deep Neural Networks and Adversarial Robustness”, Et Al 2020
- “Radioactive Data: Tracing through Training”, Et Al 2020
- “Adversarial Examples Improve Image Recognition”, Et Al 2019
- “Universal Adversarial Triggers for Attacking and Analyzing NLP”, Et Al 2019
- “Robustness Properties of Facebook’s ResNeXt WSL Models”, 2019
- “Intriguing Properties of Adversarial Training at Scale”, 2019
- “Adversarially Robust Generalization Just Requires More Unlabeled Data”, Et Al 2019
- “Are Labels Required for Improving Adversarial Robustness?”, Et Al 2019
- “Adversarial Policies: Attacking Deep Reinforcement Learning”, Et Al 2019
- “Adversarial Examples Are Not Bugs, They Are Features”, Et Al 2019
- “Benchmarking Neural Network Robustness to Common Corruptions and Perturbations”, 2019
- “Evolving Super Stimuli for Real Neurons Using Deep Generative Networks”, Et Al 2019
- “Adversarial Reprogramming of Text Classification Neural Networks”, Et Al 2018
- “Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations”, 2018
- “Adversarial Reprogramming of Neural Networks”, Et Al 2018
- “Towards the First Adversarially Robust Neural Network Model on MNIST”, Et Al 2018
- “Sensitivity and Generalization in Neural Networks: an Empirical Study”, Et Al 2018
- “Adversarial Vulnerability for Any Classifier”, Et Al 2018
- “Intriguing Properties of Adversarial Examples”, Et Al 2018
- “First-order Adversarial Vulnerability of Neural Networks and Input Dimension”, Simon-Et Al 2018
- “Adversarial Spheres”, Et Al 2018
- “CycleGAN, a Master of Steganography”, Et Al 2017
- “Adversarial Phenomenon in the Eyes of Bayesian Deep Learning”, Et Al 2017
- “Mitigating Adversarial Effects Through Randomization”, Et Al 2017
- “Learning Universal Adversarial Perturbations With Generative Models”, 2017
- “Robust Physical-World Attacks on Deep Learning Models”, Et Al 2017
- “Towards Deep Learning Models Resistant to Adversarial Attacks”, Et Al 2017
- “Ensemble Adversarial Training: Attacks and Defenses”, Et Al 2017
- “The Space of Transferable Adversarial Examples”, Et Al 2017
- “Adversarial Examples in the Physical World”, Et Al 2016
- “Foveation-based Mechanisms Alleviate Adversarial Examples”, Et Al 2015
- “Explaining and Harnessing Adversarial Examples”, Et Al 2014
- “The New CLIP Adversarial Examples Are Partially from the Use-mention Distinction. CLIP Was Trained to Predict Which Caption from a List Matches an Image. It Makes Sense That a Picture of an Apple With a Large IPod’ Label Would Be Captioned With ’iPod”
- “Pixels Still Beat Text: Attacking the OpenAI CLIP Model With Text Patches and Adversarial Pixel Perturbations”
- “A Universal Law of Robustness”
- “Apple or IPod? Easy Fix for Adversarial Textual Attacks on OpenAI’s CLIP Model!”
- “A Law of Robustness and the Importance of Overparameterization in Deep Learning”
- Wikipedia
- Miscellaneous
- Link Bibliography
See Also
Links
“TrojText: Test-time Invisible Textual Trojan Insertion”, Et Al 2023
“TrojText: Test-time Invisible Textual Trojan Insertion”, 2023-03-03 (similar; bibliography)
“Facial Misrecognition Systems: Simple Weight Manipulations Force DNNs to Err Only on Specific Persons”, 2023
“Facial Misrecognition Systems: Simple Weight Manipulations Force DNNs to Err Only on Specific Persons”, 2023-01-08 ( ; similar)
“TrojanPuzzle: Covertly Poisoning Code-Suggestion Models”, Et Al 2023
“TrojanPuzzle: Covertly Poisoning Code-Suggestion Models”, 2023-01-06 ( ; similar)
“SNAFUE: Diagnostics for Deep Neural Networks With Automated Copy/Paste Attacks”, Et Al 2022
“SNAFUE: Diagnostics for Deep Neural Networks with Automated Copy/Paste Attacks”, 2022-11-18 (similar)
“Are AlphaZero-like Agents Robust to Adversarial Perturbations?”, Et Al 2022
“Are AlphaZero-like Agents Robust to Adversarial Perturbations?”, 2022-11-07 ( ; similar; bibliography)
“Rickrolling the Artist: Injecting Invisible Backdoors into Text-Guided Image Generation Models”, Et Al 2022
“Rickrolling the Artist: Injecting Invisible Backdoors into Text-Guided Image Generation Models”, 2022-11-04 ( ; similar)
“BTD: Decompiling X86 Deep Neural Network Executables”, Et Al 2022
“BTD: Decompiling x86 Deep Neural Network Executables”, 2022-10-03 ( ; similar)
“Discovering Bugs in Vision Models Using Off-the-shelf Image Generation and Captioning”, Et Al 2022
“Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning”, 2022-08-18 ( ; similar; bibliography)
“Flatten the Curve: Efficiently Training Low-Curvature Neural Networks”, Et Al 2022
“Flatten the Curve: Efficiently Training Low-Curvature Neural Networks”, 2022-06-14 (similar)
“Why Robust Generalization in Deep Learning Is Difficult: Perspective of Expressive Power”, Et Al 2022
“Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power”, 2022-05-27 ( ; similar)
“Planting Undetectable Backdoors in Machine Learning Models”, Et Al 2022
“Planting Undetectable Backdoors in Machine Learning Models”, 2022-04-14 ( ; similar)
“Transfer Attacks Revisited: A Large-Scale Empirical Study in Real Computer Vision Settings”, Et Al 2022
“Transfer Attacks Revisited: A Large-Scale Empirical Study in Real Computer Vision Settings”, 2022-04-07 (similar)
“On the Effectiveness of Dataset Watermarking in Adversarial Settings”, 2022
“On the Effectiveness of Dataset Watermarking in Adversarial Settings”, 2022-02-25 ( ; similar)
“An Equivalence Between Data Poisoning and Byzantine Gradient Attacks”, Et Al 2022
“An Equivalence Between Data Poisoning and Byzantine Gradient Attacks”, 2022-02-17 (similar)
“Red Teaming Language Models With Language Models”, Et Al 2022
“Red Teaming Language Models with Language Models”, 2022-02-07 (similar)
“WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation”, Et Al 2022
“WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation”, 2022-01-16 ( ; similar; bibliography)
“CommonsenseQA 2.0: Exposing the Limits of AI through Gamification”, Et Al 2022
“CommonsenseQA 2.0: Exposing the Limits of AI through Gamification”, 2022-01-14 ( ; similar; bibliography)
“Models in the Loop: Aiding Crowdworkers With Generative Annotation Assistants”, Et Al 2021
“Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants”, 2021-12-16 ( ; similar)
“Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs”, 2021
“Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs”, 2021-12-16 ( ; similar)
“PROMPT WAYWARDNESS: The Curious Case of Discretized Interpretation of Continuous Prompts”, Et Al 2021
“PROMPT WAYWARDNESS: The Curious Case of Discretized Interpretation of Continuous Prompts”, 2021-12-15 ( ; similar)
“TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems”, Et Al 2021
“TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems”, 2021-11-19 ( ; similar)
“AugMax: Adversarial Composition of Random Augmentations for Robust Training”, Et Al 2021
“AugMax: Adversarial Composition of Random Augmentations for Robust Training”, 2021-10-26 (similar; bibliography)
“Unrestricted Adversarial Attacks on ImageNet Competition”, Et Al 2021
“Unrestricted Adversarial Attacks on ImageNet Competition”, 2021-10-17 (similar)
“Partial Success in Closing the Gap between Human and Machine Vision”, Et Al 2021
“Partial success in closing the gap between human and machine vision”, 2021-06-14 ( ; backlinks; similar; bibliography)
“A Universal Law of Robustness via Isoperimetry”, 2021
“A Universal Law of Robustness via Isoperimetry”, 2021-05-26 ( ; backlinks; similar; bibliography)
“Manipulating SGD With Data Ordering Attacks”, Et Al 2021
“Manipulating SGD with Data Ordering Attacks”, 2021-04-19 (similar)
“Gradient-based Adversarial Attacks against Text Transformers”, Et Al 2021
“Gradient-based Adversarial Attacks against Text Transformers”, 2021-04-15 ( ; similar)
“A Law of Robustness for Two-layers Neural Networks”, Et Al 2021
“A law of robustness for two-layers neural networks”, 2021-03-05 ( ; backlinks; similar)
“Multimodal Neurons in Artificial Neural Networks [CLIP]”, Et Al 2021
“Multimodal Neurons in Artificial Neural Networks [CLIP]”, 2021-03-04 ( ; similar; bibliography)
“Bot-Adversarial Dialogue for Safe Conversational Agents”, Et Al 2021
“Bot-Adversarial Dialogue for Safe Conversational Agents”, 2021 ( ; similar; bibliography)
“Unadversarial Examples: Designing Objects for Robust Vision”, Et Al 2020
“Unadversarial Examples: Designing Objects for Robust Vision”, 2020-12-22 (backlinks; similar)
“Concealed Data Poisoning Attacks on NLP Models”, Et Al 2020
“Concealed Data Poisoning Attacks on NLP Models”, 2020-10-23 (similar)
“Recipes for Safety in Open-domain Chatbots”, Et Al 2020
“Recipes for Safety in Open-domain Chatbots”, 2020-10-14 ( ; similar)
“Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples”, Et Al 2020
“Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples”, 2020-10-07 ( ; similar)
“Dataset Cartography: Mapping and Diagnosing Datasets With Training Dynamics”, Et Al 2020
“Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics”, 2020-09-22 ( ; similar)
“Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)”, El-Et Al 2020
“Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)”, 2020-08-03 (backlinks; similar)
“Do Adversarially Robust ImageNet Models Transfer Better?”, Et Al 2020
“Do Adversarially Robust ImageNet Models Transfer Better?”, 2020-07-16 (similar)
“Smooth Adversarial Training”, Et Al 2020
“Smooth Adversarial Training”, 2020-06-25 (similar; bibliography)
“Sponge Examples: Energy-Latency Attacks on Neural Networks”, Et Al 2020
“Sponge Examples: Energy-Latency Attacks on Neural Networks”, 2020-06-05 (similar)
“Improving the Interpretability of FMRI Decoding Using Deep Neural Networks and Adversarial Robustness”, Et Al 2020
“Improving the Interpretability of fMRI Decoding using Deep Neural Networks and Adversarial Robustness”, 2020-04-23 ( ; similar)
“Radioactive Data: Tracing through Training”, Et Al 2020
“Radioactive data: tracing through training”, 2020-02-03 (backlinks; similar; bibliography)
“Adversarial Examples Improve Image Recognition”, Et Al 2019
“Adversarial Examples Improve Image Recognition”, 2019-11-21 (similar; bibliography)
“Universal Adversarial Triggers for Attacking and Analyzing NLP”, Et Al 2019
“Universal Adversarial Triggers for Attacking and Analyzing NLP”, 2019-08-20 ( ; similar)
“Robustness Properties of Facebook’s ResNeXt WSL Models”, 2019
“Robustness properties of Facebook’s ResNeXt WSL models”, 2019-07-17 ( ; backlinks; similar)
“Intriguing Properties of Adversarial Training at Scale”, 2019
“Intriguing properties of adversarial training at scale”, 2019-06-10 ( ; backlinks; similar)
“Adversarially Robust Generalization Just Requires More Unlabeled Data”, Et Al 2019
“Adversarially Robust Generalization Just Requires More Unlabeled Data”, 2019-06-03 ( ; similar)
“Are Labels Required for Improving Adversarial Robustness?”, Et Al 2019
“Are Labels Required for Improving Adversarial Robustness?”, 2019-05-31 (similar)
“Adversarial Policies: Attacking Deep Reinforcement Learning”, Et Al 2019
“Adversarial Policies: Attacking Deep Reinforcement Learning”, 2019-05-25 ( ; similar)
“Adversarial Examples Are Not Bugs, They Are Features”, Et Al 2019
“Adversarial Examples Are Not Bugs, They Are Features”, 2019-05-06 ( ; backlinks; similar)
“Benchmarking Neural Network Robustness to Common Corruptions and Perturbations”, 2019
“Benchmarking Neural Network Robustness to Common Corruptions and Perturbations”, 2019-03-28 ( ; backlinks; similar)
“Evolving Super Stimuli for Real Neurons Using Deep Generative Networks”, Et Al 2019
“Evolving super stimuli for real neurons using deep generative networks”, 2019-01-17 ( ; similar)
“Adversarial Reprogramming of Text Classification Neural Networks”, Et Al 2018
“Adversarial Reprogramming of Text Classification Neural Networks”, 2018-09-06 ( ; backlinks; similar)
“Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations”, 2018
“Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations”, 2018-07-04 ( ; backlinks; similar)
“Adversarial Reprogramming of Neural Networks”, Et Al 2018
“Adversarial Reprogramming of Neural Networks”, 2018-06-28 ( ; backlinks; similar)
“Towards the First Adversarially Robust Neural Network Model on MNIST”, Et Al 2018
“Towards the first adversarially robust neural network model on MNIST”, 2018-05-23 (similar)
“Sensitivity and Generalization in Neural Networks: an Empirical Study”, Et Al 2018
“Sensitivity and Generalization in Neural Networks: an Empirical Study”, 2018-02-23 ( ; similar)
“Adversarial Vulnerability for Any Classifier”, Et Al 2018
“Adversarial vulnerability for any classifier”, 2018-02-23 (similar)
“Intriguing Properties of Adversarial Examples”, Et Al 2018
“Intriguing Properties of Adversarial Examples”, 2018-02-15 (similar)
“First-order Adversarial Vulnerability of Neural Networks and Input Dimension”, Simon-Et Al 2018
“First-order Adversarial Vulnerability of Neural Networks and Input Dimension”, 2018-02-05 (similar)
“Adversarial Spheres”, Et Al 2018
“Adversarial Spheres”, 2018-01-09 (similar)
“CycleGAN, a Master of Steganography”, Et Al 2017
“CycleGAN, a Master of Steganography”, 2017-12-08 ( ; backlinks; similar)
“Adversarial Phenomenon in the Eyes of Bayesian Deep Learning”, Et Al 2017
“Adversarial Phenomenon in the Eyes of Bayesian Deep Learning”, 2017-11-22 (similar)
“Mitigating Adversarial Effects Through Randomization”, Et Al 2017
“Mitigating Adversarial Effects Through Randomization”, 2017-11-06 (similar)
“Learning Universal Adversarial Perturbations With Generative Models”, 2017
“Learning Universal Adversarial Perturbations with Generative Models”, 2017-08-17 ( ; similar)
“Robust Physical-World Attacks on Deep Learning Models”, Et Al 2017
“Robust Physical-World Attacks on Deep Learning Models”, 2017-07-27 (similar)
“Towards Deep Learning Models Resistant to Adversarial Attacks”, Et Al 2017
“Towards Deep Learning Models Resistant to Adversarial Attacks”, 2017-06-19 ( ; backlinks; similar; bibliography)
“Ensemble Adversarial Training: Attacks and Defenses”, Et Al 2017
“Ensemble Adversarial Training: Attacks and Defenses”, 2017-05-19 (similar)
“The Space of Transferable Adversarial Examples”, Et Al 2017
“The Space of Transferable Adversarial Examples”, 2017-04-11 (similar)
“Adversarial Examples in the Physical World”, Et Al 2016
“Adversarial examples in the physical world”, 2016-07-08 (similar)
“Foveation-based Mechanisms Alleviate Adversarial Examples”, Et Al 2015
“Foveation-based Mechanisms Alleviate Adversarial Examples”, 2015-11-19 ( ; backlinks; similar)
“Explaining and Harnessing Adversarial Examples”, Et Al 2014
“Explaining and Harnessing Adversarial Examples”, 2014-12-20 (similar)
“The New CLIP Adversarial Examples Are Partially from the Use-mention Distinction. CLIP Was Trained to Predict Which Caption from a List Matches an Image. It Makes Sense That a Picture of an Apple With a Large IPod’ Label Would Be Captioned With ’iPod”
“Pixels Still Beat Text: Attacking the OpenAI CLIP Model With Text Patches and Adversarial Pixel Perturbations”
“A Universal Law of Robustness”
“Apple or IPod? Easy Fix for Adversarial Textual Attacks on OpenAI’s CLIP Model!”
“A Law of Robustness and the Importance of Overparameterization in Deep Learning”
Wikipedia
Miscellaneous
Link Bibliography
-
https://arxiv.org/abs/2303.02242
: “TrojText: Test-time Invisible Textual Trojan Insertion”, Yepeng Liu, Bo Feng, Qian Lou: -
https://arxiv.org/abs/2211.03769
: “Are AlphaZero-like Agents Robust to Adversarial Perturbations?”, Li-Cheng Lan, Huan Zhang, Ti-Rong Wu, Meng-Yu Tsai, I-Chen Wu, Cho-Jui Hsieh: -
https://arxiv.org/abs/2208.08831#deepmind
: “Discovering Bugs in Vision Models Using Off-the-shelf Image Generation and Captioning”, Olivia Wiles, Isabela Albuquerque, Sven Gowal: -
https://swabhs.com/assets/pdf/wanli.pdf#allen
: “WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation”, Alisa Liu, Swabha Swayamdipta, Noah A. Smith, Yejin Choi: -
https://arxiv.org/abs/2201.05320#allen
: “CommonsenseQA 2.0: Exposing the Limits of AI through Gamification”, Alon Talmor, Ori Yoran, Ronan Le Bras, Chandra Bhagavatula, Yoav Goldberg, Yejin Choi, Jonathan Berant: -
https://arxiv.org/abs/2110.13771#nvidia
: “AugMax: Adversarial Composition of Random Augmentations for Robust Training”, Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Anima Anandkumar, Zhangyang Wang: -
https://arxiv.org/abs/2106.07411
: “Partial Success in Closing the Gap between Human and Machine Vision”, : -
https://arxiv.org/abs/2105.12806
: “A Universal Law of Robustness via Isoperimetry”, Sébastien Bubeck, Mark Sellke: -
https://distill.pub/2021/multimodal-neurons/#openai
: “Multimodal Neurons in Artificial Neural Networks [CLIP]”, : -
https://aclanthology.org/2021.naacl-main.235.pdf#facebook
: “Bot-Adversarial Dialogue for Safe Conversational Agents”, Jing Xu, Da Ju, Margaret Li, Y-Lan Boureau, Jason Weston, Emily Dinan: -
https://arxiv.org/abs/2006.14536#google
: “Smooth Adversarial Training”, Cihang Xie, Mingxing Tan, Boqing Gong, Alan Yuille, Quoc V. Le: -
https://arxiv.org/abs/2002.00937
: “Radioactive Data: Tracing through Training”, Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Hervé Jégou: -
https://arxiv.org/abs/1911.09665
: “Adversarial Examples Improve Image Recognition”, Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan Yuille, Quoc V. Le: -
https://arxiv.org/abs/1706.06083
: “Towards Deep Learning Models Resistant to Adversarial Attacks”, Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu: