Adversarial examples in the physical world.

Articles citing Adversarial examples in the physical world.

Title / Author	Year
Towards evaluating the robustness of neural networks N Carlini, D Wagner 2017 ieee symposium on security and privacy (sp), 39-57	2017
Practical black-box attacks against machine learning N Papernot, P McDaniel, I Goodfellow, S Jha, ZB Celik, A Swami Proceedings of the 2017 ACM on Asia conference on computer and …	2017
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples A Athalye, N Carlini, D Wagner arXiv preprint arXiv:1802.00420	2018
Ensemble adversarial training: Attacks and defenses F Tramèr, A Kurakin, N Papernot, I Goodfellow, D Boneh, P McDaniel arXiv preprint arXiv:1705.07204	2017
Adversarial examples are not easily detected: Bypassing ten detection methods N Carlini, D Wagner Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security …	2017
A survey of methods for explaining black box models R Guidotti, A Monreale, S Ruggieri, F Turini, F Giannotti, D Pedreschi ACM computing surveys (CSUR) 51 (5), 1-42	2018
Synthesizing robust adversarial examples A Athalye, L Engstrom, A Ilyas, K Kwok International conference on machine learning, 284-293	2018
Feature squeezing: Detecting adversarial examples in deep neural networks W Xu, D Evans, Y Qi arXiv preprint arXiv:1704.01155	2017
Threat of adversarial attacks on deep learning in computer vision: A survey N Akhtar, A Mian IEEE Access 6, 14410-14430	2018
Robust physical-world attacks on deep learning visual classification K Eykholt, I Evtimov, E Fernandes, B Li, A Rahmati, C Xiao, A Prakash, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …	2018
Magnet: a two-pronged defense against adversarial examples D Meng, H Chen Proceedings of the 2017 ACM SIGSAC conference on computer and communications …	2017
Defense-gan: Protecting classifiers against adversarial attacks using generative models P Samangouei, M Kabkab, R Chellappa arXiv preprint arXiv:1805.06605	2018
Adversarial examples: Attacks and defenses for deep learning X Yuan, P He, Q Zhu, X Li IEEE transactions on neural networks and learning systems 30 (9), 2805-2824	2019
On detecting adversarial perturbations JH Metzen, T Genewein, V Fischer, B Bischoff arXiv preprint arXiv:1702.04267	2017
Countering adversarial images using input transformations C Guo, M Rana, M Cisse, L Van Der Maaten arXiv preprint arXiv:1711.00117	2017
Safety verification of deep neural networks X Huang, M Kwiatkowska, S Wang, M Wu International Conference on Computer Aided Verification, 3-29	2017
Interpretable explanations of black boxes by meaningful perturbation RC Fong, A Vedaldi Proceedings of the IEEE International Conference on Computer Vision, 3429-3437	2017
Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models PY Chen, H Zhang, Y Sharma, J Yi, CJ Hsieh Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security …	2017
Boosting adversarial attacks with momentum Y Dong, F Liao, T Pang, H Su, J Zhu, X Hu, J Li Proceedings of the IEEE conference on computer vision and pattern …	2018
Audio adversarial examples: Targeted attacks on speech-to-text N Carlini, D Wagner 2018 IEEE Security and Privacy Workshops (SPW), 1-7	2018

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