awesome-adversarial-examples-dl
by chbrian
A curated list of awesome resources for adversarial examples in deep learning
AI summary
Adversarial example resources
A curated collection of resources and research papers on generating and understanding adversarial examples in deep learning for machine learning model security and robustness.
- stars
- 263
- forks
- 56
- watching
- 10
- awesome list
- 1
- entries
- 87
What's in the list
87 links in 20 sections, with live GitHub stats.activeno commit in 2y
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
- Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
Anish Athalye, Nicholas Carlini, David Wagner. arXiv preprint arXiv:1802.00420. 2018
- Adversarial Attacks and Defences Competition
Alexey Kurakin, Ian Goodfellow, Samy Bengio, Yinpeng Dong, Fangzhou Liao, Ming Liang, Tianyu Pang, Jun Zhu, Xiaolin Hu, Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, Alan Yuille, Sangxia Huang, Yao Zhao, Yuzhe Zhao, Zhonglin Han, Junjiajia Long, Yerkebulan Berdibekov, Takuya Akiba, Seiya Tokui, Motoki Abe. arXiv preprint arXiv:1802.00420. 2018
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
Defenses for Adversarial Examples / 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 Logit Pairing
Harini Kannan, Alexey Kurakin, Ian Goodfellow. arXiv preprint arXiv:1803.06373 (2018)
Defenses for Adversarial Examples / 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)
Defenses for Adversarial Examples / 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)
Defenses for Adversarial Examples / 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)
Defenses for Adversarial Examples / 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)
Defenses for Adversarial Examples / 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
Applications for Adversarial Examples / 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
Applications for Adversarial Examples / 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)
Applications for Adversarial Examples / Object Detection
- Adversarial Examples for Semantic Segmentation and Object Detection
Xie, Cihang, et al. arXiv preprint arXiv:1703.08603 (2017)
- Physical Adversarial Examples for Object Detectors
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Florian Tramer, Atul Prakash, Tadayoshi Kohno, Dawn Song. arXiv preprint arXiv:1807.07769 (2018)
Applications for Adversarial Examples / Scene Text Recognition
- Adaptive Adversarial Attack on Scene Text Recognition
Xiaoyong Yuan, Pan He, Xiaolin Andy Li. arXiv preprint arXiv:1807.03326 (2018)
Applications for Adversarial Examples / 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)
Applications for Adversarial Examples / 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
Applications for Adversarial Examples / Speech Recognition
- Targeted Adversarial Examples for Black Box Audio Systems
Rohan Taori, Amog Kamsetty, Brenton Chu, Nikita Vemuri. arXiv preprint arXiv:1805.07820 (2018)
- CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition
Xuejing Yuan, Yuxuan Chen, Yue Zhao, Yunhui Long, Xiaokang Liu, Kai Chen, Shengzhi Zhang, Heqing Huang, Xiaofeng Wang, Carl A. Gunter. USENIX Security. 2018
- Audio Adversarial Examples: Targeted Attacks on Speech-to-Text
Nicholas Carlini, David Wagner. Deep Learning and Security Workshop, 2018
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
- Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples
Yinpeng Dong, Hang Su, Jun Zhu, Fan Bao. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
- Adversarially Robust Generalization Requires More Data
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Mądry. arXiv preprint arXiv:1804.11285. 2018
- A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples
Thomas Tanay, Lewis Griffin. arXiv preprint arXiv:1608.07690. 2018
- Adversarial vulnerability for any classifier
Alhussein Fawzi, Hamza Fawzi, Omar Fawzi. arXiv preprint arXiv:1802.08686. 2018
- Adversarial Spheres
Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian Goodfellow. ICLR. 2018
Tools
- cleverhans v2.0.0: an adversarial machine learning library
Papernot, Nicolas, et al. arXiv preprint arXiv:1610.00768 (2017)
- Foolbox: A Python toolbox to benchmark the robustness of machine learning models
Jonas Rauber, Wieland Brendel, Matthias Bethge. arXiv preprint arXiv:1707.04131 (2017)
- advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch
Gavin Weiguang Ding, Luyu Wang, Xiaomeng Jin arXiv:1902.07623 (2019)
Nothing in this list matches your filter.
Featured in 1 awesome list
Each link jumps to the spot where the list mentions awesome-adversarial-examples-dl.
