A reading survey on adversarial machine learning: Adversarial attacks and their understanding
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Abstract (arXiv)
Deep Learning has empowered us to train neural networks for complex data with high performance. However, with the growing research, several vulnerabilities in neural networks have been exposed. A particular branch of research, Adversarial Machine Learning, exploits and understands some of the vulnerabilities that cause the neural networks to misclassify for near original input. A class of algorithms called adversarial attacks is proposed to make the neural networks misclassify for various tasks in different domains. With the extensive and growing research in adversarial attacks, it is crucial to understand the classification of adversarial attacks. This will help us understand the vulnerabilities in a systematic order and help us to mitigate the effects of adversarial attacks. This article provides a survey of existing adversarial attacks and their understanding based on different perspectives. We also provide a brief overview of existing adversarial defences and their limitations in mitigating the effect of adversarial attacks. Further, we conclude with a discussion on the future research directions in the field of adversarial machine learning.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 61 of its 84 references that are in the map. First posted on arXiv 7 Aug 2023.
Builds on
61 of 84 references.
References · 61
- 2014Explaining and Harnessing Adversarial Examplescited by 7,287
- 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
- 2013Intriguing properties of neural networkscited by 5,114
- 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
- 2016Adversarial examples in the physical worldcited by 1,762
- 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,510
- 2020Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attackscited by 1,442
- 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,324
- 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,315
- 2018Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examplescited by 1,259
- 2017BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chaincited by 1,052
- 2019Adversarial Examples Are Not Bugs, They Are Featurescited by 950
38 more
- 2017A Closer Look at Memorization in Deep Networkscited by 936
- 2017Ensemble Adversarial Training: Attacks and Defensescited by 833
- 2015The Limitations of Deep Learning in Adversarial Settingscited by 784
- 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 774
- 2016Universal adversarial perturbationscited by 774
- 2015Distillation as a Defense to Adversarial Perturbations against Deep Neural Networkscited by 675
- 2017ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Modelscited by 656
- 2017One pixel attack for fooling deep neural networkscited by 579
- 2020On Adaptive Attacks to Adversarial Example Defensescited by 547
- 2017Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methodscited by 542
- 2017Countering Adversarial Images using Input Transformationscited by 517
- 2017Adversarial Patchcited by 514
- 2018Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networkscited by 500
- 2017Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Modelscited by 459
- 2019On Evaluating Adversarial Robustnesscited by 442
- 2017Wild Patterns: Ten Years After the Rise of Adversarial Machine Learningcited by 411
- 2019HopSkipJumpAttack: A Query-Efficient Decision-Based Attackcited by 328
- 2018On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Modelscited by 322
- 2018Knockoff Nets: Stealing Functionality of Black-Box Modelscited by 285
- 2018Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionalitycited by 280
- 2017PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examplescited by 243
- 2018Adversarial Risk and the Dangers of Evaluating Against Weak Attackscited by 221
- 2018Adversarial Robustness Toolbox v1.0.0cited by 214
- 2019High Accuracy and High Fidelity Extraction of Neural Networkscited by 208
- 2019Towards Stable and Efficient Training of Verifiably Robust Neural Networkscited by 204
- 2016A study of the effect of JPG compression on adversarial imagescited by 196
- 2017On the (Statistical) Detection of Adversarial Examplescited by 180
- 2018The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigationcited by 159
- 2017EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examplescited by 154
- 2016Technical Report on the CleverHans v2.1.0 Adversarial Examples Librarycited by 123
- 2019Wasserstein Adversarial Examples via Projected Sinkhorn Iterationscited by 89
- 2017Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compressioncited by 86
- 2018Adversarial vulnerability for any classifiercited by 78
- 2018Sparse DNNs with Improved Adversarial Robustnesscited by 71
- 2018Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Datacited by 69
- 2019Bypassing Backdoor Detection Algorithms in Deep Learningcited by 69
- 2015Learning with a Strong Adversarycited by 68
- 2020Bullseye Polytope: A Scalable Clean-Label Poisoning Attack with Improved Transferabilitycited by 65
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