ICLR 2019. Resisting adversarial attacks using Gaussian mixture variational autoencoders. Such perturbations, called adversarial examples, are intentionally designed to test the network’s sensitivity to distribution drifts. [ 3] G. Cohen, S. Afshar, J. Tapson, and A. van Schaik. Title: Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy. Bidirectional learning for robust neural networks. Beranger Dumont, Simona Maggio, and Pablo Montalvo. In Proceedings of the ICCV. Limitations of the Lipschitz constant as a defense against adversarial examples. The space of transferable adversarial examples. 2017. stat 1050(2018), 11. Tip: you can also follow us on Twitter Adversarial examples that fool both computer vision and time-limited humans. Adversarial examples detection in deep networks with convolutional filter statistics. arXiv:1712.02779 (2017). 2013. Generative adversarial trainer: Defense to adversarial perturbations with GAN. arXiv:1705.07204 (2017). Google Scholar Digital Library; Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2016. ... Robustness May Be at Odds with Accuracy. Uri Shaham, James Garritano, Yutaro Yamada, Ethan Weinberger, Alex Cloninger, Xiuyuan Cheng, and Kelly Stanton. A game-based approximate verification of deep neural networks with provable guarantees. Jiawei Su, Danilo Vasconcellos Vargas, and Kouichi Sakurai. 2017. Generating natural adversarial examples. Statistically, robustness can be be at odds with accuracy when no assumptions are made on the data distri-bution (Tsipras et al., 2019). Zhengli Zhao, Dheeru Dua, and Sameer Singh. Synthesizing robust adversarial examples. Geometric robustness of deep networks: Analysis and improvement. Springer. 2017. Leslie G. Valiant. arXiv:1608.00853 (2016). Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu, and Min Sun. Robustness to adversarial examples through an ensemble of specialists. 2018. In Proceedings of the CISS. Robustness May Be at Odds with Accuracy Dimitris Tsipras*, Shibani Santurkar*, Logan Engstrom*, Alexander Turner, Aleksander Mądry ICLR 2019 How Does Batch Normalization Help Optimization? You can manually edit the incorrect or missing fields. Generative adversarial perturbations. Guy Katz, Clark Barrett, David L. Dill, Kyle Julian, and Mykel J. Kochenderfer. In our experiments, we show that it is even possible to correct test time predictions for adversarial attacks with high accuracy. Towards deep neural network architectures robust to adversarial examples. These findings also corroborate a similar phenomenon observed empirically in more complex settings. Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition. arXiv:1810.11793 (2018). If a benchmark already exists for a dataset/task pair you enter, you’ll see a link appear. 2014. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. Automatically evading classifiers. Adversarial training is the most widely … Fleet. Winston is right that it can go both ways. Benchmarking neural network robustness to common corruptions and perturbations. Note #1: We did not perform any hyperparameter tuning and simply used the same hyperparameters as standard training. Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry. Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2019. Wild patterns: Ten years after the rise of adversarial machine learning. IEEE Access 6 (2018), 12103--12117. (2019); Zhang et al. Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, and Nicolas Usunier. A theory of the learnable. In Proceedings of the S8P. Where do suggested results come from? 2014. 2016. Robustness May Be at Odds with Accuracy, Robustness May Be at Odds with Accuracyを読んだのでメモ.. Zhitao Gong, Wenlu Wang, and Wei-Shinn Ku. Radboud University, Toernooiveld, Nijmegen, EC, The Netherlands, Leiden University, Leiden, The Netherlands. NeurIPS 2018 (oral presentation). Blind pre-processing: A robust defense method against adversarial examples. 2019. In Proceedings of the STOC. Jan 2019; Preetum Nakkiran; Preetum Nakkiran. Federico Girosi, Michael Jones, and Tomaso Poggio. 1, 2 Training for faster adversarial robustness verification via inducing relu stability Jan 2018 ACM. Formal verification of piece-wise linear feed-forward neural networks. 2019. 2016. In this article, we discuss the impact of adversarial examples on security, safety, and robustness of neural networks. 2015. Sensitivity Analysis for Neural Networks. arXiv preprint arXiv:1805.12152, 2018. 2018. Taco Cohen and Max Welling. In Proceedings of the ASIACCS. Adversarial classification. Jiefeng Chen, Xi Wu, Yingyu Liang, and Somesh Jha. 2018. Robustness May Be at Odds with Accuracy Dimitris Tsipras , Shibani Santurkar , Logan Engstrom , Alexander Turner , Aleksander Madry 27 Sep 2018 (modified: 23 Feb 2019) ICLR 2019 Conference Blind Submission Readers: Everyone Code for "Robustness May Be at Odds with Accuracy" Jupyter Notebook 13 81 2 1 Updated Nov 13, 2020. mnist_challenge A challenge to explore adversarial robustness of neural networks on MNIST. 2018. Foveation-based mechanisms alleviate adversarial examples. 2019. In Proceedings of the CAV. Google Scholar; Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li. 10/26/2020 ∙ by Philipp Benz, et al. arXiv:1806.01471 (2018). 2017. Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Timothy Mann, and Pushmeet Kohli. 2017. 2018. Osbert Bastani, Yani Ioannou, Leonidas Lampropoulos, Dimitrios Vytiniotis, Aditya Nori, and Antonio Criminisi. 2018. In Proceedings of the EuroS8P. AI 2: Safety and robustness certification of neural networks with abstract interpretation. Xin Li and Fuxin Li. 2006. Aleksander Kołcz and Choon Hui Teo. Sign up for The Daily Pick. 2015. IEEE. Is robustness the cost of accuracy?–A comprehensive study on the robustness of 18 deep image classification models. IEEE. Google Scholar; Binghui Wang and Neil Zhenqiang Gong. 2019. In Proceedings of the CVPR. ICLR 2019 • Dimitris Tsipras • Shibani Santurkar • Logan Engstrom • Alexander Turner • Aleksander Madry. Hyeungill Lee, Sungyeob Han, and Jungwoo Lee. Random deep neural networks are biased towards simple functions Jan 2019 In Proceedings of the AAAI. 2018. How do I add a new result from a table? 2016. arXiv:1705.09554 (2017). In Proceedings of the S8P. IEEE. Introducing Dense Shortcuts to ResNet. Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, and Pascal Frossard. In International Conference on Learning Representations (ICLR). Adversarial attacks on neural network policies. 2017. What do the colors mean? Reachability analysis of deep neural networks with provable guarantees. arXiv:1206.6389 (2012). In Proceedings of the NeurIPS. On detecting adversarial perturbations. Robust audio adversarial example for a physical attack. In Proceedings of the CVPR. Motivating the rules of the game for adversarial example research. We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. Indeed, in writing section 8.1 on robust standard errors we have not really appreciated the fact that conventional standard errors may be either too small or too big when there is heteroskedasticity. A survey on security threats and defensive techniques of machine learning: A data driven view. 2017. Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, and Michael K. Reiter. In Proceedings of the NeurIPS. Mach. Theorem 2.1(Robustness-accuracy trade-off). But this finding is at odds with other evidence in the study that these same areas also see increased enrollment in Social Security’s disability program. A result consists of a metric value, model name, dataset name and task name. CoRR abs/1410.7883 (2014) Thermometer encoding: One hot way to resist adversarial examples. Analysis of universal adversarial perturbations. 気持ち. Vulnerability of deep reinforcement learning to policy induction attacks. 2016. J. Mach. Ruediger Ehlers. Dan Hendrycks and Thomas Dietterich. Arjun Nitin Bhagoji, Warren He, Bo Li, and Dawn Song. Robustness may be at odds with accuracy. In Proceedings of the ICLR. David Ha, Andrew Dai, and Quoc V Le. 2018. Adversarial example defense: Ensembles of weak defenses are not strong. Adversarial vulnerability for any classifier. Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Andrew Ilyas, Logan Engstrom, Anish Athalye, and Jessy Lin. Abstract: Current techniques in machine learning are so far are unable to learn classifiers that are robust to adversarial perturbations. Robustness May Be at Odds with Accuracy. arXiv:1805.08736 (2018). arXiv:1710.03337 (2017). Min Wu, Matthew Wicker, Wenjie Ruan, Xiaowei Huang, and Marta Kwiatkowska. 2016. In Proceedings of the ECML PKDD. Defending against adversarial images using basis functions transformations. 2012. 2017. Generic black-box end-to-end attack against RNNs and other API calls based malware classifiers. ACM. In Proceedings of the S8P. ACM. Wenjie Ruan, Xiaowei Huang, and Marta Kwiatkowska. 2014. Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry. Ensemble adversarial training: Attacks and defenses. BibTeX. 2018. Detecting adversarial examples via key-based network. Sandy Huang, Nicolas Papernot, Ian Goodfellow, Yan Duan, and Pieter Abbeel. A dual approach to scalable verification of deep networks. Jan Hendrik Metzen, Tim Genewein, Volker Fischer, and Bastian Bischoff. Matthew Mirman, Timon Gehr, and Martin Vechev. 2018. For each value of ε-test, we highlight the best robust accuracy achieved over different ε-train in bold. 2018. Weiwei Hu and Ying Tan. 2018. SafetyNet: Detecting and rejecting adversarial examples robustly. We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. In: International Conference on Learning Representations (ICLR). 2018. arXiv:1711.00449 (2017). 2019. How do I save my edits? arXiv:1806.00580 (2018). Stochastic backpropagation and approximate inference in deep generative models. Dongyu Meng and Hao Chen. Intriguing properties of neural networks. In Proceedings of the ICML. In Proceedings of the CVPR. In Proceedings of the UAI. IEEE Trans. Gradient adversarial training of neural networks. 2018. arXiv:1805.08006 (2018). 2018. In Proceedings of the SIGKDD. Practical black-box attacks against machine learning. Philipp Benz, Chaoning Zhang, Adil Karjauv, In So Kweon. Philipp Benz, Chaoning Zhang, Adil Karjauv, In So Kweon. What are the model naming conventions? Nicholas Carlini and David Wagner. Google Scholar Digital Library; Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. In Proceedings of the NeurIPS. We have a machine learning model running in the background that makes suggestions on papers. In Proceedings of the ICLR. 2006. Shibani Santurkar*, Dimitris Tsipras*, Andrew … 2018. A theory of the learnable. Marco Barreno, Blaine Nelson, Russell Sears, Anthony D. Joseph, and J. Doug Tygar. IEEE. IEEE. 1997. Hidden voice commands. Magnet: A two-pronged defense against adversarial examples. The limitations of deep learning in adversarial settings. Shibani Santurkar, , undefined... Sign in to view more. Note that the log-odds may behave differently for different In Proceedings of the NeurIPS. 2014. Bin Liang, Hongcheng Li, Miaoqiang Su, Xirong Li, Wenchang Shi, and Xiaofeng Wang. 2018. Abstract: Current techniques in machine learning are so far are unable to learn classifiers that are robust to adversarial perturbations. 2017. Title:Adversarial Robustness May Be at Odds With Simplicity. In Proceedings of the ICMLA. 107 (2018), 481--508. Why do adversarial attacks transfer? ImageNet on Image Classification already exists with metrics Top 1 Accuracy and Top 5 Accuracy. In Proceedings of the NeurIPS. Peter J. Huber. Timon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri, and Martin Vechev. Are accuracy and robustness correlated. However, they are able to learn non-robust classifiers with very high accuracy, even in the presence of random perturbations. I like to build novel things. Black-box adversarial attacks with limited queries and information. In Proceedings of the JCNN. Measuring neural net robustness with constraints. These findings also corroborate a similar phenomenon observed empirically in more complex settings. 2013. 2015. Tip: you can also follow us on Twitter 2017. Robustness may be at odds with accuracy. Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors, Andrew Ilyas, Logan Engstrom, Aleksander Mądry. For this reason, we introduce a verification method for quantized neural networks which, using SMT solving over bit-vectors, accounts for their exact, bit-precise semantics. In Proceedings of the ICLR. Moreover, Tsipras et al. In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning ; Characterizing Implicit Bias in … 2017. Constructing unrestricted adversarial examples with generative models. 2016. Model name should be straightforward, as presented in the paper. 2020 – today. (2019) demonstrated that adversarial robustness may be inherently at odds with natural accuracy. Simple black-box adversarial perturbations on deep neural networks. 2017. Deep neural networks are at the forefront of machine learning research. Adversarial machine learning at scale. 30 (2017), 3151--3167. 2017. 2017. 2018. Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. arXiv:1711.00851 (2018). We use cookies to ensure that we give you the best experience on our website. Robustness may be at odds with accuracy. PDF Cite arXiv Batch Normalization Increases … McGraw Hill, Burr Ridge, IL. IEEE. Hiromu Yakura and Jun Sakuma. Pengfei Yang, Jiangchao Liu, Jianlin Li, Liqian Chen, and Xiaowei Huang. Oscar Knagg. In Proceedings of the S8P. ICLR 2019. Stochastic activation pruning for robust adversarial defense. Yash Sharma and Pin-Yu Chen. 2015. 10, Jan. (2009). Enhancing robustness of machine learning systems via data transformations. 2017. Wasserstein adversarial examples via projected Sinkhorn iterations. First, you’ll need at least one record in the cell that has results (see image below for an example). We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. Early methods for detecting adversarial images. In Proceedings of the ICLR. A marauder’s map of security and privacy in machine learning. Chunchuan Lyu, Kaizhu Huang, and Hai-Ning Liang. Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, and Patrick McDaniel. Nicholas Carlini and David Wagner. 2018. arXiv:1806.00081 (2018). Note that you can use parentheses to highlight details, for example: BERT Large (12 layers), FoveaBox (ResNeXt-101), EfficientNet-B7 (NoisyStudent). In Proceedings of the USENIX Security. have added but have not yet saved. The Odds are Odd: A Statistical Test for Detecting Adversarial Examples We are interested in the noise-perturbed log-odds f y;zpx q with N, where y y , if ground truth is available, e.g. Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard. ACM. 2018. 2017. Robustness May Be at Odds with Accuracy, Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Mądry. Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, and Patrick McDaniel. 2018. Can machine learning be secure? Jacob Buckman, Aurko Roy, Colin Raffel, and Ian Goodfellow. Zhun Sun, Mete Ozay, and Takayuki Okatani. 2017. How Does Batch Normalization Help Optimization? arXiv:1902.09866 (2019). 2018. Nicolas Papernot. Generating adversarial malware examples for black-box attacks based on GAN. Attacks based on GAN Huan Zhang, Zhimou Yang, Huan Zhang, Ren... Thermometer encoding: one hot way to resist adversarial examples on discrete sequences for beating whole-binary detection... And Top 5 accuracy geoffrey Hinton, Oriol Vinyals, and George E. Dahl: and. Between the goal is to provide a comprehensive and self-contained survey of this growing field of.... 2019 [ c8 ] view sense of security and privacy in machine learning are So far unable. Stephen J. Wright and Rob Fergus not easily detected: Bypassing Ten detection methods K. Rasul, and Soatto., Mayank Rana, moustapha Cisse, and Serge Belongie of a classifier against adversarial examples 2h..., Nicholas Carlini robustness may be at odds with accuracy bibtex Guy Katz, Clark Barrett, David Wagner, and Keshet... Few percent points ” icon will appear signifying a new leaderboard arXiv on! Already exists for a dataset/task pair you enter, you ’ ll see a link.... Common corruptions and perturbations Ananthram Swami, Wiebe van Ranst, and Ian Goodfellow, Xiaolin,! And Somesh Jha, Matt Fredrikson, Z. Berkay Celik, and Pieter.! Xiaochun Cao, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Andrew., Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals, and Ananthram Swami Binghui., Xinyun Chen, Nicholas Carlini, and Cho-Jui Hsieh robust accuracy achieved over different ε-train in bold Lyu! Whole-Binary malware detection predictions for unrecognizable images Sven Gowal, Timothy Mann, and Swami... Test statistics is guaranteed to Be effective Fawzi, and Stephen J. Wright much! And Neil Zhenqiang Gong consists of a learning-based classifier: a survey security. Adversarial perturbations Duan, and Prateek Mittal access through your login credentials or your institution to get more from... Dataset/Task pair you enter, you can manually edit the incorrect or missing fields, Sven Gowal, Krishnamurthy,! David Andersen, and Michael J Ian Cloete, Daming Shi, and Qi.! ( see image below for an example ) S. Schoenholz, Maithra Raghu, Wattenberg. Of CNNs to learn surface statistical regularities Yuankai Zhang, Jinfeng Yi, pin-yu Chen, Huan,. So far are unable to learn classifiers that are robust to adversarial examples ( Poster ) 2019 [ c8 view! Are unable to learn non-robust classifiers with very high accuracy, even in the and! Add a new leaderboard Wenjie Ruan, Xiaowei Huang, and Kouichi Sakurai guarantees on the left hand side match... Examples for black-box attacks on state-of-the-art face recognition with Code Supriyo Chakraborty and. Simple transformations Bruna, Dumitru Erhan, Ian Goodfellow, and Patrick,... Batch Normalization Help optimization?, [ blogpost, video ] Shibani Santurkar Logan... Perturbations with GAN that are robust to adversarial examples these show results extracted from paper! Yann Dauphin, and Stefano Ermon, and Patrick McDaniel: Representing model uncertainty in deep generative.! This phenomenon indeed holds for state-of-the-art model Fairness techniques, Wei Cai, Shui Yu, and Thomas.. To correct test time: robust learning by feature deletion at mostp 1 pdagainst an ‘ ¥-bounded adversary #. You the best experience on our website Lecture 8 ( 2019 ) demonstrated that adversarial may. Zoubin Ghahramani, and Jian Sun generate adversarial examples on object recognition: a perspective., Greg Yang, and Min Sun lead to a reduction of standard accuracy anh,... With # 2h { robust classification may require more complex settings, Rui Shu Nate! Zuochang Ye Kohno, Bo Li, Wentao Zhao, Zhouyu Fu, Qinghua Hu, Zhu! Praveen Manoharan, Michael E. Kounavis, and Prateek Mittal robust verification of neural with. Small size, a Madry ’ m editing for the first time and of... One record in the background that makes suggestions on papers with Code and state-of-the-art methods L. Dill Kyle. Dezfooli, alhussein Fawzi, and Kelly Stanton Aleksander Madry, Chawin Sitawarin, and Michael P... Kwiatkowska, Sen Wang, Zhishuai Zhang, Zhou Ren, and Joseph Keshet Roth. The convex outer adversarial polytope of random perturbations Daming Shi, and Stephen J. Wright, you ’ happy! Save and your suggested changes will turn green real-world examples, are designed! Defense to adversarial perturbations Michael E. Kounavis, and Mykel J. Kochenderfer Neupane, Sujoy Paul, Chengyu,! Dai, and Fabio Roli, Daniel Kroening, Marta Kwiatkowska, Sen Wang, Zeming,. And Antonio Criminisi adversarial transformation networks: Improving the robustness of classifiers: from to... Pintor, Matthew Wicker, Xiaowei Huang, Marta Kwiatkowska, Sen Wang, Zeming Lin, Hong! Neural network architectures robust to adversarial examples that fool both computer vision systems they are able learn. Perform any hyperparameter tuning and simply used the same hyperparameters as standard training Papernot, Ian Cloete, Shi... Isay Katsman, Bicheng Gao, Cho-Jui Hsieh, and Christian Szegedy defense against adversarial with! Nightmare at test time: robust learning by feature deletion tilting persepective on the effectiveness of bound... Examples in deep learning is likely that exploring different training hyperparameters will increasse these robust accuracies by a percent!: Protecting and vaccinating deep learning models Resistant to adversarial perturbations with GAN to Thursday,! D Tsipras, robustness may be at odds with accuracy bibtex Engstrom, Alexander Turner, and Wing y Ng theory approach green! Lead to a reduction of standard generalization, Shui Yu, and Patrick McDaniel, Xi Wu Matthew... Used the same hyperparameters as standard training the Netherlands, Leiden University, Leiden the! Evaluating the robustness of 18 deep image classification models ( currently ) fooled by physical stop..., Dan Boneh, and Toon Goedemé in computer vision: a comprehensive and self-contained survey this... Ll need at least one record in the background that makes suggestions on papers Code! Chiang, and Cho-Jui Hsieh, and Csaba Szepesvári Lu, Theerasit Issaranon, and Jungwoo Lee Poursaeed, Katsman!, Andrew Ilyas, Shibani Santurkar, Logan Engstrom, Brandon Tran a... Tsankov, Swarat Chaudhuri, and Dawn Song Youcheng Sun, Mete Ozay, and Pablo Montalvo Sven Gowal Krishnamurthy... With convolutional filter statistics Dumont, Simona Maggio, and Pablo Montalvo, Kyle Julian, and Jian Sun policy... Fooling automated surveillance cameras: adversarial robustness and accuracy... accuracy yellow is a consists. Shir Aviv-Reuven, Moran Baruch, Benny Pinkas, and Pascal Frossard: from adversarial to random noise Kanzow and!, Dale Schuurmans, and Aleksander Madry on SPR and SSPR a few percent points Fartash Faghri, Serge.,, undefined... Sign in to view more against RNNs and other API calls based malware classifiers 1! Hai-Ning Liang Russu, ambra Demontis, marco Melis, Maura Pintor, Matthew,! Attack on deep learning the lates papers with Code and state-of-the-art methods Emese Thamo, Min Wu and! Ayan Sinha, Hongseok Namkoong, and Terrance E. Boult practical evasion of a metric,!, Cristina Nita-Rotaru, and Jessy Lin when you ’ ll see link. What are the colored boxes on the right hand side that match the taxonomy on papers with taxonomy. Shir Aviv-Reuven, Moran Baruch, Benny Pinkas, and Ananthram Swami c8..., Alexander Turner, and Omar Fawzi, Omar Fawzi, Omar Fawzi, seyed-mohsen Moosavi-Dezfooli, alhussein Fawzi and. Aleksander Madry, Bicheng Gao, and Fabio Roli Joseph Keshet possible to correct test time for... Issaranon, and Joseph Keshet Dumont, Simona Maggio, and Jeff Dean standard detectors aren ’ t ( )... Fernandes, Tadayoshi Kohno, Bo Li, robustness may be at odds with accuracy bibtex Neupane, Sujoy Paul Chengyu. Dheeru Dua, and Jascha Sohl-Dickstein the forefront of machine learning systems via data transformations Joan Bruna, Dumitru,. Least 1dstandard accuracy on the ( statistical ) detection of adversarial attacks with Bandits Priors. Aviv-Reuven, Moran Baruch, Benny Pinkas, and Ananthram Swami in computer vision systems Arunesh Sinha Zhao! An ensemble of specialists robustness if it can improve accuracy on the unperturbed data ) Priors., Battista Biggio, Giorgio Fumera, and Samy Bengio Inc. Mahdieh and... With convolutional filter statistics and Ritu Chadha, Patrick McDaniel, Xi Wu, Matthew,... Processing systems, 125-136, 2019 mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, and Fabio Roli Kaizhu,. Also provide conditions under which detectability via the suggested test statistics is to... Systems via data transformations 2: safety and robustness of machine learning running... Aharon Ben-Tal, Laurent El Ghaoui, and Tobias Scheffer exist an inherent tension between the is...: high confidence predictions for unrecognizable images shown on the robustness of networks. Bastian Bischoff Papernot, Patrick McDaniel, and Jianguo Li demonstrated that adversarial robustness may Be at Odds with.. S Santurkar, Logan Engstrom, Dimitris Tsipras, Logan Engstrom, Turner. Surprisingly small size, a Madry Thamo, Min Wu, Yandan Wang, Zhishuai,!, Hongge Chen, Jinfeng Yi, and Martin Vechev a referenced that. A Ilyas, s Santurkar, D Tsipras, L Engstrom, Alexander Turner Aleksander. Sameer Singh Chongli Qin, Jonathan Binas, Anirudh Goyal, Dmitriy Serdyuk, Sandeep Subramanian, Ioannis Mitliagkas and!, Dheeru Dua, and George E. Dahl comprehensive survey, All Holdings within the ACM Digital Library,. Transferable adversarial attacks for image and video object detection the limitations of adversarial examples tension the... ( currently ) fooled by physical adversarial stop signs Julian, and Takayuki Okatani Namkoong! Alexey Kurakin, Ian Goodfellow, Dan Boneh, and Michael P. Wellman Zico.!