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Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods

Carlini, Nicholas, Wagner, David·2017·Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security
DOI 10.1145/3128572.3140444
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  1. 10.1007/s10994-010-5188-5 · DOI
  2. 10.1145/1128817.1128824 · DOI
  3. Osbert Bastani Yani Ioannou Leonidas Lampropoulos Dimitrios Vytiniotis Aditya Nori and Antonio Criminisi. 2016. Measuring neural net robustness with constraints. Advances In Neural Information Processing Systems. 2613--2621. Osbert Bastani Yani Ioannou Leonidas Lampropoulos Dimitrios Vytiniotis Aditya Nori and Antonio Criminisi. 2016. Measuring neural net robustness with constraints. Advances In Neural Information Processing Systems. 2613--2621.
  4. Arjun Nitin Bhagoji Daniel Cullina and Prateek Mittal. 2017. Dimensionality Reduction as a Defense against Evasion Attacks on Machine Learning Classifiers. arXiv preprint arXiv:1704:02654 (2017). Arjun Nitin Bhagoji Daniel Cullina and Prateek Mittal. 2017. Dimensionality Reduction as a Defense against Evasion Attacks on Machine Learning Classifiers. arXiv preprint arXiv:1704:02654 (2017).
  5. Mariusz Bojarski Davide Del Testa Daniel Dworakowski Bernhard Firner Beat Flepp Prasoon Goyal Lawrence D Jackel Mathew Monfort Urs Muller Jiakai Zhang and others. 2016. End to End Learning for Self-Driving Cars. arXiv preprint arXiv:1604.07316 (2016). Mariusz Bojarski Davide Del Testa Daniel Dworakowski Bernhard Firner Beat Flepp Prasoon Goyal Lawrence D Jackel Mathew Monfort Urs Muller Jiakai Zhang and others. 2016. End to End Learning for Self-Driving Cars. arXiv preprint arXiv:1604.07316 (2016). · arXiv
  6. 10.1093/bioinformatics/btl242 · DOI · Paper page
  7. 10.1145/1014052.1014066 · DOI
  8. Deng Jia, 2009, CVPR 2009. IEEE Conference on. IEEE, 248--255
  9. Reuben Feinman Ryan R. Curtin Saurabh Shintre and Andrew B. Gardner. 2017. Detecting Adversarial Samples from Artifacts. arXiv preprint arXiv:1703.00410 (2017). Reuben Feinman Ryan R. Curtin Saurabh Shintre and Andrew B. Gardner. 2017. Detecting Adversarial Samples from Artifacts. arXiv preprint arXiv:1703.00410 (2017). · arXiv
  10. Zhitao Gong Wenlu Wang and Wei-Shinn Ku. 2017. Adversarial and Clean Data Are Not Twins. arXiv preprint arXiv:1704.04960 (2017). Zhitao Gong Wenlu Wang and Wei-Shinn Ku. 2017. Adversarial and Clean Data Are Not Twins. arXiv preprint arXiv:1704.04960 (2017). · arXiv
  11. Gretton Arthur, 2012, A kernel two-sample test, Journal of Machine Learning Research, 13, 723
  12. Kathrin Grosse Praveen Manoharan Nicolas Papernot Michael Backes and Patrick McDaniel. 2017. On the (Statistical) Detection of Adversarial Examples. arXiv preprint arXiv:1702.06280 (2017). Kathrin Grosse Praveen Manoharan Nicolas Papernot Michael Backes and Patrick McDaniel. 2017. On the (Statistical) Detection of Adversarial Examples. arXiv preprint arXiv:1702.06280 (2017). · arXiv
  13. Shixiang Gu and Luca Rigazio. 2014. Towards deep neural network architectures robust to adversarial examples. arXiv preprint arXiv:1412.5068 (2014). Shixiang Gu and Luca Rigazio. 2014. Towards deep neural network architectures robust to adversarial examples. arXiv preprint arXiv:1412.5068 (2014). · arXiv
  14. Metzen Jan Hendrik, 2017, On Detecting Adversarial Perturbations. In International Conference on Learning Representations. shownotearXiv preprint arXiv:1702
  15. Hendrycks Dan, 2017, Early Methods for Detecting Adversarial Images. In International Conference on Learning Representations (Workshop Track).
  16. Ruitong Huang Bing Xu Dale Schuurmans and Csaba Szepesvári. 2015. Learning with a strong adversary. CoRR abs/1511.03034 (2015). Ruitong Huang Bing Xu Dale Schuurmans and Csaba Szepesvári. 2015. Learning with a strong adversary. CoRR abs/1511.03034 (2015). · arXiv
  17. Jonghoon Jin Aysegul Dundar and Eugenio Culurciello. 2015. Robust Convolutional Neural Networks under Adversarial Noise. arXiv preprint arXiv:1511.06306 (2015). Jonghoon Jin Aysegul Dundar and Eugenio Culurciello. 2015. Robust Convolutional Neural Networks under Adversarial Noise. arXiv preprint arXiv:1511.06306 (2015). · arXiv
  18. Xin Li and Fuxin Li. 2016. Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics. arXiv preprint arXiv:1612.07767 (2016). Xin Li and Fuxin Li. 2016. Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics. arXiv preprint arXiv:1612.07767 (2016). · arXiv
  19. Daniel Lowd and Christopher Meek. 2005. Adversarial learning Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. ACM 641--647. Daniel Lowd and Christopher Meek. 2005. Adversarial learning Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. ACM 641--647. · DOI · Paper page
  20. Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi and Pascal Frossard. 2016. Deepfool: a simple and accurate method to fool deep neural networks Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2574--2582. Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi and Pascal Frossard. 2016. Deepfool: a simple and accurate method to fool deep neural networks Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2574--2582. · DOI · Paper page
  21. Vinod Nair and Geoffrey E. Hinton. 2010. Rectified linear units improve restricted boltzmann machines Proceedings of the 27th international conference on machine learning (ICML-10). 807--814. Vinod Nair and Geoffrey E. Hinton. 2010. Rectified linear units improve restricted boltzmann machines Proceedings of the 27th international conference on machine learning (ICML-10). 807--814.
  22. 10.1214/aos/1176343082 · DOI
  23. Nicolas Papernot Patrick McDaniel and Ian Goodfellow. 2016. Transferability in machine learning: from phenomena to black-box attacks using adversarial samples. arXiv preprint arXiv:1605.07277 (2016). Nicolas Papernot Patrick McDaniel and Ian Goodfellow. 2016. Transferability in machine learning: from phenomena to black-box attacks using adversarial samples. arXiv preprint arXiv:1605.07277 (2016). · arXiv
  24. Papernot Nicolas, 2016, 2016 IEEE European Symposium on. IEEE, 372--387
  25. 10.1109/SP.2016.41 · DOI
  26. Petrov Slav, 2016, Announcing syntaxnet: The world's most accurate parser goes open source, Google Research Blog, 12, 2016
  27. Andras Rozsa Ethan M. Rudd and Terrance E. Boult. 2016. Adversarial diversity and hard positive generation Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 25--32. Andras Rozsa Ethan M. Rudd and Terrance E. Boult. 2016. Adversarial diversity and hard positive generation Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 25--32. · DOI
  28. Uri Shaham Yutaro Yamada and Sahand Negahban. 2015. Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization. arXiv preprint arXiv:1511.05432 (2015). Uri Shaham Yutaro Yamada and Sahand Negahban. 2015. Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization. arXiv preprint arXiv:1511.05432 (2015). · arXiv
  29. Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox and Martin Riedmiller. 2015. Striving for simplicity: The all convolutional net International Conference on Learning Representations (Workshop Track). Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox and Martin Riedmiller. 2015. Striving for simplicity: The all convolutional net International Conference on Learning Representations (Workshop Track).
  30. 10.5555/2627435.2670313 · DOI
  31. Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jon Shlens and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2818--2826. Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jon Shlens and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2818--2826. · DOI · Paper page
  32. Yonghui Wu Mike Schuster Zhifeng Chen Quoc V. Le Mohammad Norouzi Wolfgang Macherey Maxim Krikun Yuan Cao Qin Gao Klaus Macherey and others. 2016. Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016). Yonghui Wu Mike Schuster Zhifeng Chen Quoc V. Le Mohammad Norouzi Wolfgang Macherey Maxim Krikun Yuan Cao Qin Gao Klaus Macherey and others. 2016. Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016). · arXiv
  33. Stephan Zheng Yang Song Thomas Leung and Ian Goodfellow. 2016. Improving the robustness of deep neural networks via stability training Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4480--4488. Stephan Zheng Yang Song Thomas Leung and Ian Goodfellow. 2016. Improving the robustness of deep neural networks via stability training Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4480--4488. · DOI

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Metadata

Authors
Carlini, N., & Wagner, D.
Affiliations
University of California, Berkeley, Berkeley, CA, USA
Venue
Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security
Publisher
ACM
Published
2017-11-03
Pages
3-14
DOI
10.1145/3128572.3140444
Cited by
966
References
7
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Funders
  • Qualcomm
  • AFOSR (MURI award FA9550-12-1-0040)
  • Intel (ISTC for Secure Computing)
  • Hewlett Foundation (Center for Long-Term Cybersecurity)

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Carlini, N., & Wagner, D. (2017). Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods. Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security. https://doi.org/10.1145/3128572.3140444