adversarial examples (AI) tag

Gwern Branwen

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“Foundational Challenges in Assuring Alignment and Safety of Large Language Models”, Anwar et al 2024

Foundational Challenges in Assuring Alignment and Safety of Large Language Models

“Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression”, Hong et al 2024

Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression

“Logits of API-Protected LLMs Leak Proprietary Information”, Finlayson et al 2024

Logits of API-Protected LLMs Leak Proprietary Information

“Syntactic Ghost: An Imperceptible General-Purpose Backdoor Attacks on Pre-Trained Language Models”, Cheng et al 2024

Syntactic Ghost: An Imperceptible General-purpose Backdoor Attacks on Pre-trained Language Models

“Fast Adversarial Attacks on Language Models In One GPU Minute”, Sadasivan et al 2024

Fast Adversarial Attacks on Language Models In One GPU Minute

“`ArtPrompt`: ASCII Art-Based Jailbreak Attacks against Aligned LLMs”, Jiang et al 2024

ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs

“Using Hallucinations to Bypass GPT-4’s Filter”, Lemkin 2024

Using Hallucinations to Bypass GPT-4’s Filter

“Discovering Universal Semantic Triggers for Text-To-Image Synthesis”, Zhai et al 2024

Discovering Universal Semantic Triggers for Text-to-Image Synthesis

“Sleeper Agents: Training Deceptive LLMs That Persist Through Safety Training”, Hubinger et al 2024

Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training

“May the Noise Be With You: Adversarial Training without Adversarial Examples”, Arous et al 2023

May the Noise be with you: Adversarial Training without Adversarial Examples

“Universal Jailbreak Backdoors from Poisoned Human Feedback”, Rando & Tramèr 2023

Universal Jailbreak Backdoors from Poisoned Human Feedback

“Language Model Inversion”, Morris et al 2023

Language Model Inversion

“Summon a Demon and Bind It: A Grounded Theory of LLM Red Teaming in the Wild”, Inie et al 2023

Summon a Demon and Bind it: A Grounded Theory of LLM Red Teaming in the Wild

“Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game”, Toyer et al 2023

Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game

“Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competition”, Schulhoff et al 2023

Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competition

“Prompt-Specific Poisoning Attacks on Text-To-Image Generative Models”, Shan et al 2023

Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models

“PAIR: Jailbreaking Black Box Large Language Models in 20 Queries”, Chao et al 2023

PAIR: Jailbreaking Black Box Large Language Models in 20 Queries

“Low-Resource Languages Jailbreak GPT-4”, Yong et al 2023

Low-Resource Languages Jailbreak GPT-4

“Consistency Trajectory Models (CTM): Learning Probability Flow ODE Trajectory of Diffusion”, Kim et al 2023

Consistency Trajectory Models (CTM): Learning Probability Flow ODE Trajectory of Diffusion

“How Robust Is Google’s Bard to Adversarial Image Attacks?”, Dong et al 2023

How Robust is Google’s Bard to Adversarial Image Attacks?

“Why Do Universal Adversarial Attacks Work on Large Language Models?: Geometry Might Be the Answer”, Subhash et al 2023

Why do universal adversarial attacks work on large language models?: Geometry might be the answer

“Investigating the Existence of ‘Secret Language’ in Language Models”, Wang et al 2023

Investigating the Existence of ‘Secret Language’ in Language Models

“Prompts Should Not Be Seen As Secrets: Systematically Measuring Prompt Extraction Attack Success”, Zhang & Ippolito 2023

Prompts Should not be Seen as Secrets: Systematically Measuring Prompt Extraction Attack Success

“CLIPMasterPrints: Fooling Contrastive Language-Image Pre-Training Using Latent Variable Evolution”, Freiberger et al 2023

CLIPMasterPrints: Fooling Contrastive Language-Image Pre-training Using Latent Variable Evolution

“On the Exploitability of Instruction Tuning”, Shu et al 2023

On the Exploitability of Instruction Tuning

“Evaluating Superhuman Models With Consistency Checks”, Fluri et al 2023

Evaluating Superhuman Models with Consistency Checks

“Large Language Models Sometimes Generate Purely Negatively-Reinforced Text”, Roger 2023

Large Language Models Sometimes Generate Purely Negatively-Reinforced Text

“TrojText: Test-Time Invisible Textual Trojan Insertion”, Liu et al 2023

TrojText: Test-time Invisible Textual Trojan Insertion

“Glaze: Protecting Artists from Style Mimicry by Text-To-Image Models”, Shan et al 2023

Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models

“Facial Misrecognition Systems: Simple Weight Manipulations Force DNNs to Err Only on Specific Persons”, Zehavi & Shamir 2023

Facial Misrecognition Systems: Simple Weight Manipulations Force DNNs to Err Only on Specific Persons

“TrojanPuzzle: Covertly Poisoning Code-Suggestion Models”, Aghakhani et al 2023

TrojanPuzzle: Covertly Poisoning Code-Suggestion Models

“SNAFUE: Diagnostics for Deep Neural Networks With Automated Copy/Paste Attacks”, Casper et al 2022

SNAFUE: Diagnostics for Deep Neural Networks with Automated Copy/Paste Attacks

“Are AlphaZero-Like Agents Robust to Adversarial Perturbations?”, Lan et al 2022

Are AlphaZero-like Agents Robust to Adversarial Perturbations?

“Rickrolling the Artist: Injecting Invisible Backdoors into Text-Guided Image Generation Models”, Struppek et al 2022

Rickrolling the Artist: Injecting Invisible Backdoors into Text-Guided Image Generation Models

“Broken Neural Scaling Laws”, Caballero et al 2022

Broken Neural Scaling Laws

“On Optimal Learning Under Targeted Data Poisoning”, Hanneke et al 2022

On Optimal Learning Under Targeted Data Poisoning

“BTD: Decompiling X86 Deep Neural Network Executables”, Liu et al 2022

BTD: Decompiling x86 Deep Neural Network Executables

“Discovering Bugs in Vision Models Using Off-The-Shelf Image Generation and Captioning”, Wiles et al 2022

Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning

“Adversarially Trained Neural Representations May Already Be As Robust As Corresponding Biological Neural Representations”, Guo et al 2022

Adversarially trained neural representations may already be as robust as corresponding biological neural representations

“Flatten the Curve: Efficiently Training Low-Curvature Neural Networks”, Srinivas et al 2022

Flatten the Curve: Efficiently Training Low-Curvature Neural Networks

“Why Robust Generalization in Deep Learning Is Difficult: Perspective of Expressive Power”, Li et al 2022

Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power

“Diffusion Models for Adversarial Purification”, Nie et al 2022

Diffusion Models for Adversarial Purification

“Planting Undetectable Backdoors in Machine Learning Models”, Goldwasser et al 2022

Planting Undetectable Backdoors in Machine Learning Models

“Transfer Attacks Revisited: A Large-Scale Empirical Study in Real Computer Vision Settings”, Mao et al 2022

Transfer Attacks Revisited: A Large-Scale Empirical Study in Real Computer Vision Settings

“On the Effectiveness of Dataset Watermarking in Adversarial Settings”, Tekgul & Asokan 2022

On the Effectiveness of Dataset Watermarking in Adversarial Settings

“An Equivalence Between Data Poisoning and Byzantine Gradient Attacks”, Farhadkhani et al 2022

An Equivalence Between Data Poisoning and Byzantine Gradient Attacks

“Red Teaming Language Models With Language Models”, Perez et al 2022

Red Teaming Language Models with Language Models

“WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation”, Liu et al 2022

WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation

“CommonsenseQA 2.0: Exposing the Limits of AI through Gamification”, Talmor et al 2022

CommonsenseQA 2.0: Exposing the Limits of AI through Gamification

“Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs”, Korkmaz 2021

Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs

“Models in the Loop: Aiding Crowdworkers With Generative Annotation Assistants”, Bartolo et al 2021

Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants

“PROMPT WAYWARDNESS: The Curious Case of Discretized Interpretation of Continuous Prompts”, Khashabi et al 2021

PROMPT WAYWARDNESS: The Curious Case of Discretized Interpretation of Continuous Prompts

“Spinning Language Models for Propaganda-As-A-Service”, Bagdasaryan & Shmatikov 2021

Spinning Language Models for Propaganda-As-A-Service

“TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems”, Doan et al 2021

TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems

“AugMax: Adversarial Composition of Random Augmentations for Robust Training”, Wang et al 2021

AugMax: Adversarial Composition of Random Augmentations for Robust Training

“Unrestricted Adversarial Attacks on ImageNet Competition”, Chen et al 2021

Unrestricted Adversarial Attacks on ImageNet Competition

“Partial Success in Closing the Gap between Human and Machine Vision”, Geirhos et al 2021

Partial success in closing the gap between human and machine vision

“A Universal Law of Robustness via Isoperimetry”, Bubeck & Sellke 2021

A Universal Law of Robustness via Isoperimetry

“Manipulating SGD With Data Ordering Attacks”, Shumailov et al 2021

Manipulating SGD with Data Ordering Attacks

“Gradient-Based Adversarial Attacks against Text Transformers”, Guo et al 2021

Gradient-based Adversarial Attacks against Text Transformers

“A Law of Robustness for Two-Layers Neural Networks”, Bubeck et al 2021

A law of robustness for two-layers neural networks

“Multimodal Neurons in Artificial Neural Networks [CLIP]”, Goh et al 2021

Multimodal Neurons in Artificial Neural Networks [CLIP]

“Do Input Gradients Highlight Discriminative Features?”, Shah et al 2021

Do Input Gradients Highlight Discriminative Features?

“Words As a Window: Using Word Embeddings to Explore the Learned Representations of Convolutional Neural Networks”, Dharmaretnam et al 2021

Words as a window: Using word embeddings to explore the learned representations of Convolutional Neural Networks

“Bot-Adversarial Dialogue for Safe Conversational Agents”, Xu et al 2021

Bot-Adversarial Dialogue for Safe Conversational Agents

“Unadversarial Examples: Designing Objects for Robust Vision”, Salman et al 2020

Unadversarial Examples: Designing Objects for Robust Vision

“Concealed Data Poisoning Attacks on NLP Models”, Wallace et al 2020

Concealed Data Poisoning Attacks on NLP Models

“Recipes for Safety in Open-Domain Chatbots”, Xu et al 2020

Recipes for Safety in Open-domain Chatbots

“Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples”, Gowal et al 2020

Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples

“Dataset Cartography: Mapping and Diagnosing Datasets With Training Dynamics”, Swayamdipta et al 2020

Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics

“Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)”, El-Mhamdi et al 2020

Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)

“Do Adversarially Robust ImageNet Models Transfer Better?”, Salman et al 2020

Do Adversarially Robust ImageNet Models Transfer Better?

“Smooth Adversarial Training”, Xie et al 2020

Smooth Adversarial Training

“Sponge Examples: Energy-Latency Attacks on Neural Networks”, Shumailov et al 2020

Sponge Examples: Energy-Latency Attacks on Neural Networks

“Improving the Interpretability of FMRI Decoding Using Deep Neural Networks and Adversarial Robustness”, McClure et al 2020

Improving the Interpretability of fMRI Decoding using Deep Neural Networks and Adversarial Robustness

“Approximate Exploitability: Learning a Best Response in Large Games”, Timbers et al 2020

Approximate exploitability: Learning a best response in large games

“Radioactive Data: Tracing through Training”, Sablayrolles et al 2020

Radioactive data: tracing through training

“Adversarial Examples Improve Image Recognition”, Xie et al 2019

Adversarial Examples Improve Image Recognition

“Fooling LIME and SHAP: Adversarial Attacks on Post Hoc Explanation Methods”, Slack et al 2019

Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods

“The Bouncer Problem: Challenges to Remote Explainability”, Merrer & Tredan 2019

The Bouncer Problem: Challenges to Remote Explainability

“Distributionally Robust Language Modeling”, Oren et al 2019

Distributionally Robust Language Modeling

“Universal Adversarial Triggers for Attacking and Analyzing NLP”, Wallace et al 2019

Universal Adversarial Triggers for Attacking and Analyzing NLP

“Robustness Properties of Facebook’s ResNeXt WSL Models”, Orhan 2019

Robustness properties of Facebook’s ResNeXt WSL models

“Intriguing Properties of Adversarial Training at Scale”, Xie & Yuille 2019

Intriguing properties of adversarial training at scale

“Adversarially Robust Generalization Just Requires More Unlabeled Data”, Zhai et al 2019

Adversarially Robust Generalization Just Requires More Unlabeled Data

“Are Labels Required for Improving Adversarial Robustness?”, Uesato et al 2019

Are Labels Required for Improving Adversarial Robustness?

“Adversarial Policies: Attacking Deep Reinforcement Learning”, Gleave et al 2019

Adversarial Policies: Attacking Deep Reinforcement Learning

“Adversarial Examples Are Not Bugs, They Are Features”, Ilyas et al 2019

Adversarial Examples Are Not Bugs, They Are Features

“Smooth Adversarial Examples”, Zhang et al 2019

Smooth Adversarial Examples

“Benchmarking Neural Network Robustness to Common Corruptions and Perturbations”, Hendrycks & Dietterich 2019

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

“Fairwashing: the Risk of Rationalization”, Aïvodji et al 2019

Fairwashing: the risk of rationalization

“AdVersarial: Perceptual Ad Blocking Meets Adversarial Machine Learning”, Tramèr et al 2018

AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning

“Adversarial Reprogramming of Text Classification Neural Networks”, Neekhara et al 2018

Adversarial Reprogramming of Text Classification Neural Networks

“Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations”, Hendrycks & Dietterich 2018

Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations

“Adversarial Reprogramming of Neural Networks”, Elsayed et al 2018

Adversarial Reprogramming of Neural Networks

“Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data”, Yang et al 2018

Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data

“Towards the First Adversarially Robust Neural Network Model on MNIST”, Schott et al 2018

Towards the first adversarially robust neural network model on MNIST

“Adversarial Vulnerability for Any Classifier”, Fawzi et al 2018

Adversarial vulnerability for any classifier

“Sensitivity and Generalization in Neural Networks: an Empirical Study”, Novak et al 2018

Sensitivity and Generalization in Neural Networks: an Empirical Study

“Intriguing Properties of Adversarial Examples”, Cubuk et al 2018

Intriguing Properties of Adversarial Examples

“First-Order Adversarial Vulnerability of Neural Networks and Input Dimension”, Simon-Gabriel et al 2018

First-order Adversarial Vulnerability of Neural Networks and Input Dimension

“Adversarial Spheres”, Gilmer et al 2018

Adversarial Spheres

“CycleGAN, a Master of Steganography”, Chu et al 2017

CycleGAN, a Master of Steganography

“Adversarial Phenomenon in the Eyes of Bayesian Deep Learning”, Rawat et al 2017

Adversarial Phenomenon in the Eyes of Bayesian Deep Learning

“Mitigating Adversarial Effects Through Randomization”, Xie et al 2017

Mitigating Adversarial Effects Through Randomization

“Learning Universal Adversarial Perturbations With Generative Models”, Hayes & Danezis 2017

Learning Universal Adversarial Perturbations with Generative Models

“Robust Physical-World Attacks on Deep Learning Models”, Eykholt et al 2017

Robust Physical-World Attacks on Deep Learning Models

“Lempel-Ziv: a ‘1-Bit Catastrophe’ but Not a Tragedy”, Lagarde & Perifel 2017

Lempel-Ziv: a ‘1-bit catastrophe’ but not a tragedy

“Towards Deep Learning Models Resistant to Adversarial Attacks”, Madry et al 2017

Towards Deep Learning Models Resistant to Adversarial Attacks

“Ensemble Adversarial Training: Attacks and Defenses”, Tramèr et al 2017

Ensemble Adversarial Training: Attacks and Defenses

“The Space of Transferable Adversarial Examples”, Tramèr et al 2017

The Space of Transferable Adversarial Examples

“Learning from Simulated and Unsupervised Images through Adversarial Training”, Shrivastava et al 2016

Learning from Simulated and Unsupervised Images through Adversarial Training

“Membership Inference Attacks against Machine Learning Models”, Shokri et al 2016

Membership Inference Attacks against Machine Learning Models

“Adversarial Examples in the Physical World”, Kurakin et al 2016

Adversarial examples in the physical world

“Foveation-Based Mechanisms Alleviate Adversarial Examples”, Luo et al 2015

Foveation-based Mechanisms Alleviate Adversarial Examples

“Explaining and Harnessing Adversarial Examples”, Goodfellow et al 2014

Explaining and Harnessing Adversarial Examples

“Pixels Still Beat Text: Attacking the OpenAI CLIP Model With Text Patches and Adversarial Pixel Perturbations”

Pixels still beat text: Attacking the OpenAI CLIP model with text patches and adversarial pixel perturbations

NoaNabeshima

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’, not ‘Granny Smith’!

“A Universal Law of Robustness”

A Universal Law of Robustness

“Apple or IPod? Easy Fix for Adversarial Textual Attacks on OpenAI's CLIP Model!”

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”

A law of robustness and the importance of overparameterization in deep learning

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Wikipedia

Miscellaneous

Link Bibliography

https://arxiv.org/abs/2402.15570: “Fast Adversarial Attacks on Language Models In One GPU Minute”, Vinu Sankar Sadasivan, Shoumik Saha, Gaurang Sriramanan, Priyatham Kattakinda, Atoosa Chegini, Soheil Feizi

link-bibliography
https://arxiv.org/abs/2401.05566#anthropic: “Sleeper Agents: Training Deceptive LLMs That Persist Through Safety Training”, Evan Hubinger, Carson Denison, Jesse Mu, Mike Lambert, Meg Tong, Monte MacDiarmid, Tamera Lanham, Daniel M. Ziegler

link-bibliography
https://arxiv.org/abs/2310.08419: “PAIR: Jailbreaking Black Box Large Language Models in 20 Queries”, Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, Eric Wong

link-bibliography
https://arxiv.org/abs/2310.02279#sony: “Consistency Trajectory Models (CTM): Learning Probability Flow ODE Trajectory of Diffusion”, Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka, Yutong He

link-bibliography
https://arxiv.org/abs/2309.11751: “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, Jun Zhu

link-bibliography
https://arxiv.org/abs/2306.07567: “Large Language Models Sometimes Generate Purely Negatively-Reinforced Text”, Fabien Roger

link-bibliography
https://arxiv.org/abs/2303.02242: “TrojText: Test-Time Invisible Textual Trojan Insertion”, Yepeng Liu, Bo Feng, Qian Lou

link-bibliography
https://arxiv.org/abs/2302.04222: “Glaze: Protecting Artists from Style Mimicry by Text-To-Image Models”, Shawn Shan, Jenna Cryan, Emily Wenger, Haitao Zheng, Rana Hanocka, Ben Y. Zhao

link-bibliography
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

link-bibliography
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

link-bibliography
https://arxiv.org/abs/2205.07460: “Diffusion Models for Adversarial Purification”, Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, Anima Anandkumar

link-bibliography
https://swabhs.com/assets/pdf/wanli.pdf#allen: “WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation”, Alisa Liu, Swabha Swayamdipta, <a href=https://nasmith.github.io/>N. A. Smith</a>, Yejin Choi

link-bibliography
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

link-bibliography
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

link-bibliography
https://arxiv.org/abs/2106.07411: “Partial Success in Closing the Gap between Human and Machine Vision”, Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Tizian Thieringer, Matthias Bethge, Felix A. Wichmann

link-bibliography
https://arxiv.org/abs/2105.12806: “A Universal Law of Robustness via Isoperimetry”, Sébastien Bubeck, Mark Sellke

link-bibliography
https://distill.pub/2021/multimodal-neurons/#openai: “Multimodal Neurons in Artificial Neural Networks [CLIP]”, Gabriel Goh, Nick Cammarata, Chelsea Voss, Shan Carter, Michael Petrov, Ludwig Schubert, Alec Radford

link-bibliography
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

link-bibliography
https://arxiv.org/abs/2006.14536#google: “Smooth Adversarial Training”, Cihang Xie, Mingxing Tan, Boqing Gong, Alan Yuille, Quoc V. Le

link-bibliography
https://arxiv.org/abs/2002.00937: “Radioactive Data: Tracing through Training”, Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Hervé Jégou

link-bibliography
https://arxiv.org/abs/1911.09665: “Adversarial Examples Improve Image Recognition”, Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan Yuille, Quoc V. Le

link-bibliography
https://arxiv.org/abs/1706.06083: “Towards Deep Learning Models Resistant to Adversarial Attacks”, Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu

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