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A curated list of awesome resources for adversarial examples in deep learning

Awesome Adversarial Examples for Deep Learning

A list of amazing resources for adversarial examples in deep learning

Cite this work

If you find this list useful for academic research, we would appreciate citations for our survey paper:

@article{yuan2017adversarial,
  title={Adversarial Examples: Attacks and Defenses for Deep Learning},
  author={Xiaoyong Yuan, Pan He, Qile Zhu, Rajendra Rana Bhat, Xiaolin Li},
  journal={arXiv preprint arXiv:1712.07107},
  year={2017}
}

Adversarial Examples for Machine Learning

• The security of machine learning Barreno, Marco, et al. Machine Learning 81.2 (2010): 121-148.
• Adversarial classification Dalvi, Nilesh, et al. Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2004.
• Adversarial learning Lowd, Daniel, and Christopher Meek. Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. ACM, 2005.
• Multiple classifier systems for robust classifier design in adversarial environments Biggio, Battista, Giorgio Fumera, and Fabio Roli. International Journal of Machine Learning and Cybernetics 1.1-4 (2010): 27-41.
• Evasion Attacks against Machine Learning at Test Time Biggio, Battista, et al. Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Berlin, Heidelberg, 2013.
• Can machine learning be secure? Barreno, Marco, et al. Proceedings of the 2006 ACM Symposium on Information, computer and communications security. ACM, 2006.
• Towards the science of security and privacy in machine learning Papernot, Nicolas, et al. arXiv preprint arXiv:1611.03814 (2016).
• Pattern recognition systems under attack Roli, Fabio, Battista Biggio, and Giorgio Fumera. Iberoamerican Congress on Pattern Recognition. Springer, Berlin, Heidelberg, 2013.

Approaches for Generating Adversarial Examples in Deep Learning

• Intriguing properties of neural networks Szegedy, Christian, et al. arXiv preprint arXiv:1312.6199 (2013).
• Explaining and harnessing adversarial examples Goodfellow, Ian J., Jonathon Shlens, and Christian Szegedy. arXiv preprint arXiv:1412.6572 (2014).
• Deep neural networks are easily fooled: High confidence predictions for unrecognizable images Nguyen, Anh, Jason Yosinski, and Jeff Clune. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
• Adversarial examples in the physical world Kurakin, Alexey, Ian Goodfellow, and Samy Bengio. arXiv preprint arXiv:1607.02533 (2016).
• Adversarial diversity and hard positive generation Rozsa, Andras, Ethan M. Rudd, and Terrance E. Boult. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2016.
• The limitations of deep learning in adversarial settings Papernot, Nicolas, et al. Security and Privacy (EuroS&P), 2016 IEEE European Symposium on. IEEE, 2016.
• Adversarial manipulation of deep representations Sabour, Sara, et al. ICLR. 2016.
• Deepfool: a simple and accurate method to fool deep neural networks Moosavi-Dezfooli, Seyed-Mohsen, Alhussein Fawzi, and Pascal Frossard. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.
• Universal adversarial perturbations Moosavi-Dezfooli, Seyed-Mohsen, et al. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017.
• Towards evaluating the robustness of neural networks Carlini, Nicholas, and David Wagner. Security and Privacy (S&P). 2017.
• Machine Learning as an Adversarial Service: Learning Black-Box Adversarial Examples Hayes, Jamie, and George Danezis. arXiv preprint arXiv:1708.05207 (2017).
• Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models Chen, Pin-Yu, et al. 10th ACM Workshop on Artificial Intelligence and Security (AISEC) with the 24th ACM Conference on Computer and Communications Security (CCS). 2017.
• Ground-Truth Adversarial Examples Carlini, Nicholas, et al. arXiv preprint arXiv:1709.10207 (2017).
• Generating Natural Adversarial Examples Zhao, Zhengli, Dheeru Dua, and Sameer Singh. arXiv preprint arXiv:1710.11342 (2017).

Defenses for Adversarial Examples

Network Ditillation

• Distillation as a defense to adversarial perturbations against deep neural networks Papernot, Nicolas, et al.Security and Privacy (SP), 2016 IEEE Symposium on. IEEE, 2016.

Adversarial (Re)Training

• Learning with a strong adversary Huang, Ruitong, et al. arXiv preprint arXiv:1511.03034 (2015).
• Adversarial machine learning at scale Kurakin, Alexey, Ian Goodfellow, and Samy Bengio. ICLR. 2017.
• Ensemble Adversarial Training: Attacks and Defenses Tramèr, Florian, et al. arXiv preprint arXiv:1705.07204 (2017).
• Adversarial training for relation extraction Wu, Yi, David Bamman, and Stuart Russell. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017.

Adversarial Detecting

• Detecting Adversarial Samples from Artifacts Feinman, Reuben, et al. arXiv preprint arXiv:1703.00410 (2017).
• Adversarial and Clean Data Are Not Twins Gong, Zhitao, Wenlu Wang, and Wei-Shinn Ku. arXiv preprint arXiv:1704.04960 (2017).
• Safetynet: Detecting and rejecting adversarial examples robustly Lu, Jiajun, Theerasit Issaranon, and David Forsyth. ICCV (2017).
• On the (statistical) detection of adversarial examples Grosse, Kathrin, et al. arXiv preprint arXiv:1702.06280 (2017).
• On detecting adversarial perturbations Metzen, Jan Hendrik, et al. ICLR Poster. 2017.
• Early Methods for Detecting Adversarial Images Hendrycks, Dan, and Kevin Gimpel. ICLR Workshop (2017).
• Dimensionality Reduction as a Defense against Evasion Attacks on Machine Learning Classifiers Bhagoji, Arjun Nitin, Daniel Cullina, and Prateek Mittal. arXiv preprint arXiv:1704.02654 (2017).
• Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight Lin, Yen-Chen, et al. arXiv preprint arXiv:1710.00814 (2017).
• PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples Song, Yang, et al. arXiv preprint arXiv:1710.10766 (2017).

Input Reconstruction

• PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples Song, Yang, et al. arXiv preprint arXiv:1710.10766 (2017).
• MagNet: a Two-Pronged Defense against Adversarial Examples Meng, Dongyu, and Hao Chen. CCS (2017).
• Towards deep neural network architectures robust to adversarial examples Gu, Shixiang, and Luca Rigazio. arXiv preprint arXiv:1412.5068 (2014).

Classifier Robustifying

• Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks Bradshaw, John, Alexander G. de G. Matthews, and Zoubin Ghahramani.arXiv preprint arXiv:1707.02476 (2017).
• Robustness to Adversarial Examples through an Ensemble of Specialists Abbasi, Mahdieh, and Christian Gagné. arXiv preprint arXiv:1702.06856 (2017).

Network Verification

• Reluplex: An efficient SMT solver for verifying deep neural networks Katz, Guy, et al. CAV 2017.
• Safety verification of deep neural networks Huang, Xiaowei, et al. International Conference on Computer Aided Verification. Springer, Cham, 2017.
• Towards proving the adversarial robustness of deep neural networks Katz, Guy, et al. arXiv preprint arXiv:1709.02802 (2017).
• Deepsafe: A data-driven approach for checking adversarial robustness in neural networks Gopinath, Divya, et al. arXiv preprint arXiv:1710.00486 (2017).
• DeepXplore: Automated Whitebox Testing of Deep Learning Systems Pei, Kexin, et al. arXiv preprint arXiv:1705.06640 (2017).

Others

• Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong He, Warren, et al. 11th USENIX Workshop on Offensive Technologies (WOOT 17). (2017).
• Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Carlini, Nicholas, and David Wagner. AISec. 2017.

Applications for Adversarial Examples

Reinforcement Learning

• Adversarial attacks on neural network policies Huang, Sandy, et al. arXiv preprint arXiv:1702.02284 (2017).
• Delving into adversarial attacks on deep policies Kos, Jernej, and Dawn Song. ICLR Workshop. 2017.

Generative Modelling

• Adversarial examples for generative models Kos, Jernej, Ian Fischer, and Dawn Song. arXiv preprint arXiv:1702.06832 (2017).
• Adversarial images for variational autoencoders Tabacof, Pedro, Julia Tavares, and Eduardo Valle. Workshop on Adversarial Training, NIPS. 2016

Semantic Segmentation

• Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition Sharif, Mahmood, et al. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2016.
• Adversarial Examples for Semantic Segmentation and Object Detection Xie, Cihang, et al. arXiv preprint arXiv:1703.08603 (2017).
• Adversarial Examples for Semantic Image Segmentation Fischer, Volker, et al. ICLR workshop. 2017.
• Universal Adversarial Perturbations Against Semantic Image Segmentation Hendrik Metzen, Jan, et al. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017.
• Semantic Image Synthesis via Adversarial Learning Dong, Hao, et al. ICCV(2017).

Object Detection

• Adversarial Examples for Semantic Segmentation and Object Detection Xie, Cihang, et al. arXiv preprint arXiv:1703.08603 (2017).

Reading Comprehension

• Adversarial examples for evaluating reading comprehension systems Jia, Robin, and Percy Liang. EMNLP. 2017.
• Understanding Neural Networks through Representation Erasure Li, Jiwei, Will Monroe, and Dan Jurafsky. arXiv preprint arXiv:1612.08220 (2016).

Malware Detection

• Adversarial examples for malware detection Grosse, Kathrin, et al. European Symposium on Research in Computer Security. Springer, Cham, 2017.
• Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN Hu, Weiwei, and Ying Tan. arXiv preprint arXiv:1702.05983 (2017).
• Evading Machine Learning Malware Detection Anderson, Hyrum S., et al. Black Hat. 2017.
• DeepDGA: Adversarially-Tuned Domain Generation and Detection Anderson, Hyrum S., Jonathan Woodbridge, and Bobby Filar. Proceedings of the 2016 ACM Workshop on Artificial Intelligence and Security. ACM, 2016.
• Automatically evading classifiers Xu, Weilin, Yanjun Qi, and David Evans. Proceedings of the 2016 Network and Distributed Systems Symposium. 2016.

Transferability of Adversarial Examples

• Transferability in machine learning: from phenomena to black-box attacks using adversarial samples Papernot, Nicolas, Patrick McDaniel, and Ian Goodfellow. arXiv preprint arXiv:1605.07277 (2016).
• Delving into transferable adversarial examples and black-box attacks Liu, Yanpei, et al. ICLR 2017.

Analysis of Adversarial Examples

• Fundamental limits on adversarial robustness Fawzi, Alhussein, Omar Fawzi, and Pascal Frossard. Proc. ICML, Workshop on Deep Learning. 2015.
• Exploring the space of adversarial images Tabacof, Pedro, and Eduardo Valle. Neural Networks (IJCNN), 2016 International Joint Conference on. IEEE, 2016.
• A boundary tilting perspective on the phenomenon of adversarial examples Tanay, Thomas, and Lewis Griffin. arXiv preprint arXiv:1608.07690 (2016).
• Measuring neural net robustness with constraints Bastani, Osbert, et al. Advances in Neural Information Processing Systems. 2016.
• cleverhans v2.0.0: an adversarial machine learning library Papernot, Nicolas, et al. arXiv preprint arXiv:1610.00768 (2017).