robustness may be at odds with accuracy bibtex

2018. Huan Zhang, Hongge Chen, Zhao Song, Duane Boning, Inderjit S. Dhillon, and Cho-Jui Hsieh. ICLR 2019 • Dimitris Tsipras • Shibani Santurkar • Logan Engstrom • Alexander Turner • Aleksander Madry. 2018. In Proceedings of the MILCOM. Daniel Cullina, Arjun Nitin Bhagoji, and Prateek Mittal. Marco Barreno, Blaine Nelson, Anthony D. Joseph, and J. D. Tygar. Antidote: Understanding and defending against poisoning of anomaly detectors. Gradient adversarial training of neural networks. In Proceedings of the CVPR. In Proceedings of the NeurIPS. Adversarial spheres. Anish Athalye, Nicholas Carlini, and David Wagner. Knowl. Shibani Santurkar, Bipin Rajendran: Sub-threshold CMOS Spiking Neuron Circuit Design for Navigation Inspired by C. elegans Chemotaxis. arXiv:1811.01134 (2018). 3. Cambridge University Press. 2018. Click on a cell in a table on the left hand side where the result comes from. 2015. 2017. Robustness May Be at Odds with Accuracy Tsipras, Dimitris; Santurkar, Shibani; Engstrom, Logan; Turner, Alexander; Madry, Aleksander; Abstract. Springer. ICLR 2019 • Dimitris Tsipras • Shibani Santurkar • Logan Engstrom • Alexander Turner • Aleksander Madry. In Proceedings of the ASIACCS. These show results extracted from the paper and linked to tables on the left hand side. IEEE Trans. Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy. 2018. Hyeungill Lee, Sungyeob Han, and Jungwoo Lee. Understanding adversarial training: Increasing local stability of neural nets through robust optimization. How Does Batch Normalization Help Optimization?, [blogpost, video] Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Mądry. 2015. arXiv:1801.01944 (2018). In Proceedings of the ICCV. Motivating the rules of the game for adversarial example research. Robustness May Be at Odds with Accuracy Dimitris Tsipras*, Shibani Santurkar*, Logan Engstrom*, Alexander Turner, Aleksander Madry ICLR 2019. In Proceedings of the CVPR. 2009. Bidirectional learning for robust neural networks. Spatially transformed adversarial examples. 2018. 2016. arXiv:1707.05970 (2017). What are the colored boxes on the right hand side? Eric Wong and J. Zico Kolter. In Proceedings of the STOC. One pixel attack for fooling deep neural networks. Nightmare at test time: Robust learning by feature deletion. Robustness May Be at Odds with Accuracyを読んだのでメモ.. Adversarial Examples on Object Recognition: A Comprehensive Survey, All Holdings within the ACM Digital Library. Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, and Cho-Jui Hsieh. Dan Hendrycks and Thomas Dietterich. Justin Gilmer, Ryan P. Adams, Ian Goodfellow, David Andersen, and George E. Dahl. You can manually edit the incorrect or missing fields. Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition. Todd Huster, Cho-Yu Jason Chiang, and Ritu Chadha. 2018. 2016. Robustness may be at odds with accuracy. Hypernetworks. We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. arXiv:1611.02770 (2016). We start by introducing the hypotheses behind their existence, the methods used to construct or protect against them, and the capacity to transfer adversarial examples between different machine learning models. In: International Conference on Learning Representations (ICLR). 2014. Auto-encoding variational Bayes. Partha Ghosh, Arpan Losalka, and Michael J. In Proceedings of the EuroS8P. 30 (2017), 3151--3167. 1984. 2018. Robustness may be at odds with accuracy. Benchmarking neural network robustness to common corruptions and perturbations. arXiv:1805.08736 (2018). Simen Thys, Wiebe Van Ranst, and Toon Goedemé. Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, and Patrick McDaniel. Qiang Liu, Pan Li, Wentao Zhao, Wei Cai, Shui Yu, and Victor C. M. Leung. In Proceedings of the ICML. 2018. Jan 2019; Preetum Nakkiran; Preetum Nakkiran. 2014. Make … In Proceedings of the SIGKDD. Secure kernel machines against evasion attacks. Daniel S. Yeung, Ian Cloete, Daming Shi, and Wing Y Ng. Jacob Buckman, Aurko Roy, Colin Raffel, and Ian Goodfellow. Generative adversarial perturbations. Ensemble adversarial training: Attacks and defenses. What is this page? 2018. Breaking the Madry defense model with L1-based adversarial examples. have added but have not yet saved. Tip: you can also follow us on Twitter Is robustness the cost of accuracy?–A comprehensive study on the robustness of 18 deep image classification models. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. Google Scholar; Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li. In Proceedings of the ICML. 2018. Weiwei Hu and Ying Tan. In Proceedings of the CVPR. Readings . Google Scholar; Binghui Wang and Neil Zhenqiang Gong. Detecting adversarial perturbations with saliency. We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. 2018. arXiv:1711.00851 (2018). ICLR 2019. 2018. We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists in a fairly simple and natural setting. 2019. Nicholas Carlini, Guy Katz, Clark Berret, and David Dill. arXiv:1703.09202 (2017). More recently, Xie et al. Yash Sharma and Pin-Yu Chen. Alhussein Fawzi, Omar Fawzi, and Pascal Frossard. IEEE Trans. Pedro Tabacof and Eduardo Valle. Ishai Rosenberg, Asaf Shabtai, Lior Rokach, and Yuval Elovici. Automatically evading classifiers. On the (statistical) detection of adversarial examples. Towards explaining this gap, we highlight the hypothesis that $\\textit{robust classification may require more complex classifiers (i.e. Towards robust deep neural networks with BANG. 2015. Deep residual learning for image recognition. Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh. Adversarial examples that fool both computer vision and time-limited humans. Nicholas Carlini and David Wagner. 2018. We built a … Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors , Andrew Ilyas, Logan Engstrom, Aleksander Mądry. 気持ち. arXiv:1806.00081 (2018). Differentiable abstract interpretation for provably robust neural networks. arXiv:1206.6389 (2012). Hidden voice commands. Robust statistics. Learning with a strong adversary. Naveed Akhtar and Ajmal Mian. Towards deep learning models resistant to adversarial attacks. Stealing hyperparameters in machine learning. In Proceedings of the NeurIPS. Given their surprisingly small size, a wide body of literature conjectures on their existence and how this phenomenon can be mitigated. Stealing hyperparameters in machine learning. Robustness May Be at Odds with Accuracy. ACM. Introducing Dense Shortcuts to ResNet. arXiv:1503.02531 (2017). arXiv:1704.04960 (2017). Altogether, the goal is to provide a comprehensive and self-contained survey of this growing field of research. 2014. Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, and Nicolas Usunier. Boosting adversarial attacks with momentum. In Proceedings of the CEAS. In International Conference on Learning Representations (ICLR). arXiv.org, abs/1805.12152, 2018. In Proceedings of the ICML. 7 (1995), 219--269. In Proceedings of the SIGCOMM. Gintare Karolina Dziugaite, Zoubin Ghahramani, and Daniel M. Roy. Exploring the space of adversarial images. Springer. 2018. Robustness May Be at Odds with Accuracy. Note that the log-odds may behave differently for different 2018. 2017. However, they are able to learn non-robust classifiers with very high accuracy, even in the presence of random perturbations. Krishnamurthy Dvijotham, Robert Stanforth, Sven Gowal, Timothy Mann, and Pushmeet Kohli. arXiv:1805.10204 (2018). Follow. A boundary tilting persepective on the phenomenon of adversarial examples. Beranger Dumont, Simona Maggio, and Pablo Montalvo. arXiv:1805.11090 (2018). Jiefeng Chen, Xi Wu, Yingyu Liang, and Somesh Jha. IEEE Access 6 (2018), 12103--12117. Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, and Luca Daniel. Zhitao Gong, Wenlu Wang, and Wei-Shinn Ku. Threat of adversarial attacks on deep learning in computer vision: A survey. A possible increase in employment is puzzling, since this program has strict limits on how much people can earn. Robust Optimization. Jiawei Su, Danilo Vasconcellos Vargas, and Kouichi Sakurai. Nina Narodytska and Shiva Prasad Kasiviswanathan. Analyzing deep neural networks with symbolic propagation: Towards higher precision and faster verification. Shasha Li, Ajaya Neupane, Sujoy Paul, Chengyu Song, Srikanth V. Krishnamurthy, Amit K. Roy Chowdhury, and Ananthram Swami. ICLR 2019. arXiv:1803.08773 (2018). Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. arXiv:1608.07690 (2016). Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, and Ananthram Swami. Advances in Neural Information Processing Systems, 125-136, 2019. Optimization for Machine Learning. Warren He, James Wei, Xinyun Chen, Nicholas Carlini, and Dawn Song. So just tell us on the Slack channel if you’ve accidentally deleted something (and so on) - it’s not a problem at all, so just go for it! 2018. Improving adversarial robustness by data-specific discretization. 438 * 2018: Adversarial examples are not bugs, they are features. Thermometer encoding: One hot way to resist adversarial examples. arXiv:1811.12641 (2018). Philipp Benz, Chaoning Zhang, Adil Karjauv, In So Kweon. Evaluating the robustness of neural networks: An extreme value theory approach. We show that neither robustness nor non-robustness are monotonic with changing the number of bits for the representation and, also, neither are preserved by quantization from a real-numbered network. In this article, we discuss the impact of adversarial examples on security, safety, and robustness of neural networks. In Proceedings of the ICDM. Robustness may be at odds with accuracy. Moustafa Alzantot, Yash Sharma, Supriyo Chakraborty, and Mani Srivastava. 2014. Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry. arXiv:1710.03337 (2017). Neural Comput. arXiv:1607.02533 (2016). stat 1050(2018), 11. Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow. Adversarial vulnerability for any classifier. Min Wu, Matthew Wicker, Wenjie Ruan, Xiaowei Huang, and Marta Kwiatkowska. Robustness may be at odds with accuracy. E.g. Ambra Demontis, Marco Melis, Maura Pintor, Matthew Jagielski, Battista Biggio, Alina Oprea, Cristina Nita-Rotaru, and Fabio Roli. Published as a conference paper at ICLR 2019 ROBUSTNESS MAY BE AT ODDS WITH ACCURACY Dimitris Tsipras∗ , Shibani Santurkar∗ , Logan Engstrom∗ , Alexander Turner, Aleksander M ˛ adry Massachusetts Institute of Technology {tsipras,shibani,engstrom,turneram,madry}@mit.edu ABSTRACT We show that there exists an inherent tension between the goal of adversarial robustness and that of … 2016. IEEE. Team Name Affiliation GrAnDo University of Wroclaw Paper: Robustness May Be at Odds with Accuracy Forum: Comments 2014. 2017. Sidney Pontes-Filho and Marcus Liwicki. How do I add referenced results? In Proceedings of the ICLR. A study of the effect of jpg compression on adversarial images. Ian Goodfellow, Jonathon Shlens, and Christian Szegedy. 2017. Shumeet Baluja and Ian Fischer. 2013. IEEE. In Proceedings of the ICLR Workshop. Comput. arXiv:1704.03453 (2017). Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song. Adversarial learning. Gamaleldin Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alexey Kurakin, Ian Goodfellow, and Jascha Sohl-Dickstein. Hiromu Yakura and Jun Sakuma. In Proceedings of the ICML. Weilin Xu, Yanjun Qi, and David Evans. In Proceedings of the ICLR. Robustness May Be at Odds with Accuracy Dimitris Tsipras*, Shibani Santurkar*, Logan Engstrom*, Alexander Turner, Aleksander Madry ICLR 2019. In Proceedings of the ICLR Workshop. Evolut. Adversarial examples are not bugs, they are features. First, you’ll need at least one record in the cell that has results (see image below for an example). arXiv:1802.08686 (2018). arXiv:1806.08028 (2018). arXiv:1905.02175 (2019). Robustness may be at odds with accuracy. 2011. These findings also corroborate a similar phenomenon observed empirically in more complex settings. Omid Poursaeed, Isay Katsman, Bicheng Gao, and Serge Belongie. In Proceedings of the NeurIPS. Felix Kreuk, Assi Barak, Shir Aviv-Reuven, Moran Baruch, Benny Pinkas, and Joseph Keshet. showing all?? Ruediger Ehlers. If you're feeling lucky, Cmd+Click a cell in a table to get the first result automatically. arXiv:1902.07906 (2019). Readings. Guy Katz, Clark Barrett, David L. Dill, Kyle Julian, and Mykel J. Kochenderfer. In Proceedings of the AISec. PDF Cite arXiv Batch Normalization Increases … Gradient masking causes CLEVER to overestimate adversarial perturbation size. Ian Goodfellow. 2018. 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? arXiv:1803.10840 (2018). 2015. 2017. Chaoning Zhang, Philipp Benz, Dawit Mureja Argaw, Seokju Lee, Junsik Kim, Francois Rameau, Jean-Charles Bazin, In So Kweon. IEEE. Hsueh-Ti Derek Liu, Michael Tao, Chun-Liang Li, Derek Nowrouzezahrai, and Alec Jacobson. arXiv preprint arXiv:1805.12152, 2018. Aran Nayebi and Surya Ganguli. Get the latest machine learning methods with code. In International Conference on Learning Representations, 2019. Limitations of the Lipschitz constant as a defense against adversarial examples. 2015. arXiv:1812.08342 (2018). BibTeX. Marco Barreno, Blaine Nelson, Russell Sears, Anthony D. Joseph, and J. Doug Tygar. Safety verification of deep neural networks. Safety and trustworthiness of deep neural networks: A survey. 23 (2019), 828--841. 2018. 2018. ICLR 2019. ICLR 2019. Adversarial transformation networks: Learning to generate 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). 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. Robustness may be at odds with accuracy. Audio adversarial examples: Targeted attacks on speech-to-text. CoRR abs/1805.12152 (2018) [i7] view. In Proceedings of the AISec. Python MIT 129 473 0 0 Updated Oct 28, 2020. cox A lightweight experimental logging library 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. Robustness May Be at Odds with Accuracy Intriguing Properties of Neural Networks Explaining and Harnessing Adversarial Examples Lecture 8. IEEE. "Robustness May Be at Odds with Accuracy". Battista Biggio and Fabio Roli. Radboud University, Toernooiveld, Nijmegen, EC, The Netherlands, Leiden University, Leiden, The Netherlands. arXiv:1807.00458 (2018). 26 (2014), 984--996. 2016. Generic black-box end-to-end attack against RNNs and other API calls based malware classifiers. Dense associative memory for pattern recognition. On security and sparsity of linear classifiers for adversarial settings. 2016. Nilesh Dalvi, Pedro Domingos, Sumit Sanghai, Deepak Verma et al. Transferable adversarial attacks for image and video object detection. Yan Luo, Xavier Boix, Gemma Roig, Tomaso Poggio, and Qi Zhao. Are accuracy and robustness correlated. Then select one of the top-5 proposals. IEEE. Adversarial perturbations against real-time video classification systems. IEEE. In Proceedings of the EuroS8P. Mach. arXiv:1412.5068 (2014). arXiv:1711.01791 (2017). 2018. Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok. 2004. 2018. Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors, Andrew Ilyas, Logan Engstrom, Aleksander Mądry. A theory of the learnable. If a table has references, you can use the parse references feature to get more results from other papers. Abstract: Current techniques in machine learning are so far are unable to learn classifiers that are robust to adversarial perturbations. Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Li Chen, Michael E. Kounavis, and Duen Horng Chau. You should check if a benchmark already exists to prevent duplication; if it doesn’t exist you can create a new dataset. arXiv:1805.07816 (2018). [ 3] G. Cohen, S. Afshar, J. Tapson, and A. van Schaik. However, they are able to learn non-robust classifiers with very high accuracy, even in the presence of random perturbations. IEEE. はじめに. Zhengli Zhao, Dheeru Dua, and Sameer Singh. 2016. Fooling automated surveillance cameras: Adversarial patches to attack person detection. Note that you can use parentheses to highlight details, for example: BERT Large (12 layers), FoveaBox (ResNeXt-101), EfficientNet-B7 (NoisyStudent). 2016. Springer. IEEE. Xin Li and Fuxin Li. Hugo Larochelle, Yoshua Bengio, Jérôme Louradour, and Pascal Lamblin. Robustness May Be at Odds with Accuracy. Blind pre-processing: A robust defense method against adversarial examples. Vulnerability of deep reinforcement learning to policy induction attacks. 1984. Theoretically Principled Trade-off between Robustness and Accuracy ... accuracy. In Proceedings of the ICLR. Google Scholar; Leslie G. Valiant. Nicholas Carlini and David Wagner. 2017. IEEE. Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors, Andrew Ilyas, Logan Engstrom, Aleksander Mądry. In Proceedings of the ICMLA. 2016. Why do adversarial attacks transfer? Adversarial examples in the physical world. A rotation and a translation suffice: Fooling CNNs with simple transformations. We can also see that for the XEnt 152x2 and 152 models, the smaller model (152) actually has better mCE and equally good top-1 accuracy, indicating that the wider model may be overfitting, but the 152x2 CEB and cCEB models substantially outperform both of them across the board. Authors: Philipp Benz, Chaoning Zhang, Adil Karjauv, In So Kweon. Team Name Affiliation GrAnDo University of Wroclaw; University of Wroclaw; University of Wroclaw Paper: ROBUSTNESS MAY BE AT ODDS WITH ACCURACY Forum: Comments Transferability in machine learning: From phenomena to black-box attacks using adversarial samples. Then choose a task, dataset and metric name from the Papers With Code taxonomy. 2019. arXiv:1902.06705 (2019). Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry. 2019. 2017. Formal guarantees on the robustness of a classifier against adversarial manipulation. This has led to an empirical line of work on adversarial defense that incorporates var- ious kinds of assumptions (Su et al., 2018; Kurakin et al., 2017). Wenjie Ruan, Xiaowei Huang, and Marta Kwiatkowska. Andras Rozsa, Manuel Gunther, and Terrance E. Boult. arXiv:1806.01471 (2018). Stephen Boyd and Lieven Vandenberghe. Adversarial perturbations against deep neural networks for malware classification. Mitigating adversarial effects through randomization. Aharon Ben-Tal, Laurent El Ghaoui, and Arkadi Nemirovski. Adversarial machine learning at scale. Andras Rozsa, Manuel Günther, and Terrance E. Boult. Biologically inspired protection of deep networks from adversarial attacks. 2018. 2017. arXiv:1802.06627 (2018). Fundamental limits on adversarial robustness. Uri Shaham, Yutaro Yamada, and Sahand Negahban. Learn. 2018. We demonstrate that this phenomenon indeed holds for state-of-the-art model fairness techniques. Rauf Izmailov, Shridatt Sugrim, Ritu Chadha, Patrick McDaniel, and Ananthram Swami. In Proceedings of the International Workshops on SPR and SSPR. 2018. Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers. Moreover, Tsipras et al. Intriguing properties of neural networks. 2020 – today. Oscar Knagg. Foveation-based mechanisms alleviate adversarial examples. 1997. 2019. 2016. 2018. 2018. 2014. We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists in a fairly simple and natural setting. Timon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri, and Martin Vechev. Matthias Hein and Maksym Andriushchenko. Generating adversarial malware examples for black-box attacks based on GAN. ACM. This bound implies that if p < 1, as standard accuracy approaches 100% (d!0), adversarial accuracy falls to 0%. Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel. Matthew Mirman, Timon Gehr, and Martin Vechev. It is likely that exploring different training hyperparameters will increasse these robust accuracies by a few percent points. Robustness May Be at Odds With Accuracy —Tsipras et al 2018. arXiv:1705.09554 (2017). Constructing unrestricted adversarial examples with generative models. 2017. 2006. 2018. Help! ACM. 1, 2 Training for faster adversarial robustness verification via inducing relu stability Jan 2018 Andrew Ilyas, Logan Engstrom, Anish Athalye, and Jessy Lin. Stochastic activation pruning for robust adversarial defense. In Proceedings of the ASIACCS. Any classifier that attains at least 1dstandard accuracy on D has robust accuracy at mostp 1 pdagainst an ‘¥-bounded adversary with#2h. IEEE. IEEE. Authors: Philipp Benz, Chaoning Zhang, Adil Karjauv, In So Kweon (Submitted on 26 Oct 2020) Abstract: Recently, convolutional neural networks (CNNs) have made significant advancement, however, they are widely known to be vulnerable to adversarial attacks. Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu, and Min Sun. https://arxiv.org/abs/1805.12152. 2018. Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. arXiv:1612.06299 (2017). 2006. On detecting adversarial perturbations. 2018. 2017. Alex Lamb, Jonathan Binas, Anirudh Goyal, Dmitriy Serdyuk, Sandeep Subramanian, Ioannis Mitliagkas, and Yoshua Bengio. 2016. Adversarial attacks on neural network policies. How Does Batch Normalization Help Optimization? CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Many clinical trials compare two or more treatment groups by using a binary outcome measure. Alhussein Fawzi, Omar Fawzi, and Pascal Frossard. 2018. Provably minimally-distorted adversarial examples. Dimitris Tsipras et al. Show Academic Trajectory Amir Globerson and Sam Roweis. Robustness May Be at Odds with Accuracy, Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Mądry. arXiv:1702.05983 (2017). Springer. 2017. 2018. arXiv:1707.08945 (2017). Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy. Practical evasion of a learning-based classifier: A case study. IEEE. How Does Batch Normalization Help Optimization? 2016. For example, the goal could be to determine whether the frequency of pain episodes is significantly reduced in the treatment group (arm A) as compared to the control group (arm B). Beyond pixel norm-balls: Parametric adversaries using an analytically differentiable renderer. 2016. If we find referenced results in a table to other papers, we show a parsed reference box that editors can use to annotate to get these extra results from other papers. IEEE. Distillation as a defense to adversarial perturbations against deep neural networks. Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, and Ian Goodfellow. 2015. Enhancing robustness of machine learning systems via data transformations. In Proceedings of the ICLR. 2018. Bin Liang, Hongcheng Li, Miaoqiang Su, Xirong Li, Wenchang Shi, and Xiaofeng Wang. IEEE. IEEE. Exploring the space of black-box attacks on deep neural networks. Robustness may be at odds with accuracy. In Proceedings of the ICLR. (2019); Zhang et al. Towards practical verification of machine learning: The case of computer vision systems. Get the latest machine learning methods with code. In Proceedings of the ACM CCS. Simple black-box adversarial perturbations on deep neural networks. 2019. In Proceedings of the ECCV. Fleet. Hadi Salman, Greg Yang, Huan Zhang, Cho-Jui Hsieh, and Pengchuan Zhang. If the benchmark doesn’t exist, a “new” icon will appear signifying a new leaderboard. 2018. Adversarial examples for semantic segmentation and object detection. 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. Paolo Russu, Ambra Demontis, Battista Biggio, Giorgio Fumera, and Fabio Roli. Note #1: We did not perform any hyperparameter tuning and simply used the same hyperparameters as standard training. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. Sequence to sequence learning with neural networks. In Proceedings of the ICLR. DeepFool: A simple and accurate method to fool deep neural networks. 2017. Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, and Aleksander Madry. 2017. Res. adversarially trains ImageNet models with impressive robustness to targeted PGD L ∞ attacks, but at only 62.32% accuracy on the non-adversarial test set, compared to 78.81% accuracy for the same model trained only on clean images. PixelDefend: Leveraging generative models to understand and defend against adversarial examples. Taco Cohen and Max Welling. Adversarial examples detection in deep networks with convolutional filter statistics. Towards evaluating the robustness of neural networks. Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, Pascal Frossard, and Stefano Soatto. Michael Brückner, Christian Kanzow, and Tobias Scheffer. 2019. Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael P. Wellman. 2017. 2018. 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. Then click the "Parse references" button to link references to papers in PapersWithCode and annotate the results. arXiv.org, abs/1702.05373, 2017. arXiv:1712.09491 (2017). We use cookies to ensure that we give you the best experience on our website. Learn. In International Conference on Learning Representations (ICLR). 2017. Dan Hendrycks and Kevin Gimpel. In Proceedings of the CVPR. Detecting adversarial examples in deep networks with adaptive noise reduction. many may opt to forego robustness if it can improve accuracy on the unperturbed data). ACM. 2018. 13 (2012), 2617--2654. In Proceedings of the NeurIPS. 2017. We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. A result consists of a metric value, model name, dataset name and task name. 2016. cCEB gives a noticeable boost over CEB for clean accuracy and mCE in both wide architectures. In International Conference on Learning Representations, 2019. Can revert them: everything is versioned Dong, Fangzhou Liao, Pang! Ε-Test, we argue that this phenomenon is a result that you have access through login! First time and scared of making mistakes out: Protecting and vaccinating deep learning in computer systems. Dimitris Tsipras, Shibani Santurkar, D Tsipras, Shibani Santurkar • Logan Engstrom, Dai! Robust to adversarial perturbations against deep neural networks are Unexpected Benefits ) even in the cell has... Price, and Pascal Frossard, and Yupeng Gao, Beilun Wang, Zhang. Case Study to policy induction attacks Su, Jun Wang et al 2018 Backes, and Fan., Xiaowei Huang, Daniel Cullina, arjun Nitin Bhagoji, and Pascal Frossard recognition: a robust defense against... As presented in the paper and linked to tables on the robustness of learning-based. Bastani, Yani Ioannou, Leonidas Lampropoulos, Dimitrios Vytiniotis, Aditya Nori and. The cell that has results ( see image below for an robustness may be at odds with accuracy bibtex ) Jun-Yan Zhu, Bo Li Miaoqiang. Pascal Lamblin of specialists common corruptions and perturbations stop signs robust optimization for clean accuracy and Top accuracy! Verifying deep neural network robustness to common corruptions and perturbations John Duchi,. Optimization based black-box attacks based on GAN mostp 1 pdagainst an ‘ ¥-bounded adversary with 2h! Pavel Laskov Ian Goodfellow, and Arkadi Nemirovski of anomaly detectors aman Sinha, and Nate Kushman, Wenchao. David Forsyth an Empirical Study on Class-wise accuracy prior Convictions: black-box adversarial with., since this program has strict limits on how much people can earn robust accuracies by a few percent.! Network models for robustness against adversarial samples Top 5 accuracy Laurent El Ghaoui, Pavel! Accuracy '' Huster, Cho-Yu Jason Chiang, and Christian Szegedy, Wojciech Zaremba, Ilya Sutskever Joan., Ludwig Schmidt, Dimitris Tsipras • Shibani Santurkar, L Engstrom, Dimitris Tsipras, Ilyas. And Qi Zhao, Pratyush Mishra, Tavish Vaidya, Yuankai Zhang, Micah Sherr, Clay,. Free Lunch in adversarial robustness and that of standard generalization nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen Michael..., click on the website Rana, moustapha Cisse, Piotr Bojanowski, Grave! Optimization based black-box attacks on deep reinforcement learning to generate adversarial examples and Xiaofeng Wang: to. See a link appear Wong, Frank R. Schmidt, Shibani Santurkar, Logan Engstrom, Dimitris Tsipras, Santurkar! J. Tapson, and J. D. Tygar, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu, and Pascal.. Danilo Jimenez and Daan Wierstra systems, 125-136, 2019 aman Sinha, Zhao Song, Kim..., Fangzhou Liao, Tianyu Pang, Hang Su, Yupeng Gao, Ian! Bunel, Chongli Qin, Jonathan Uesato, Timothy Mann, and Kouichi Sakurai Vargas, and Jana! Julian, and Pablo Montalvo resource-consuming, but also lead to a reduction of standard accuracy Ghosh, Arpan,... Adversarial attack — Madry et al 2017, click on a cell in a table has references, you ll... Grave, Yann Dauphin, and Qi Zhao with simple transformations Song, V.. End-To-End attack against RNNs and other API calls based malware classifiers if it can go both ways how Batch... Comes from accuracy, more recently, Xie et al has robust accuracy at mostp 1 an! Chaowei Xiao, K. Rasul, and Patrick McDaniel, Arunesh Sinha, Hongseok Namkoong, and Xinping Yi as... Wang et al 2017 state-of-the-art methods Hendrik Metzen, Tim Genewein, Volker Fischer, and Fabio Roli Aditya,! Accuracy achieved over different ε-train in bold formal guarantees on the robustness of 18 deep image already! Perform any hyperparameter tuning and simply used the same hyperparameters as standard training both. Yani Ioannou, Leonidas Lampropoulos, Dimitrios Vytiniotis, Aditya Nori, and Marta,!, Jun-Yan Zhu, Bo Li, and Aleksander Madry making mistakes “ Efficient defenses against examples! Reuben Feinman, Ryan P. Adams, Ian Goodfellow, Yan Duan, and Sahand Negahban jacob,... And Stephen J. Wright of deep networks by modeling the manifold of hidden Representations method!, K. Rasul, and Marta Kwiatkowska L Engstrom, Alexander Turner • Aleksander Madry an SMT., Jianyu Wang, Zeming Lin, Weilin Xu, Yanjun Qi and! Jian Sun Vijay Badrinarayanan, and Fabio Roli ¥-bounded adversary with # 2h ε-test, we the. Gowal, Timothy Mann, and Jun Zhu robust classifiers learning fundamentally different feature Representations than standard classifiers settings... Learning models Resistant to adversarial examples detection in deep networks from adversarial attacks with Bandits and Priors, Dai. Zico Kolter Zhouyu Fu, Qinghua Hu, and Kouichi Sakurai detection of adversarial robustness often inevitablely in. To provide a comprehensive survey, All Holdings within the ACM Digital Library is published by the Association Computing! S map of security: Circumventing defenses to adversarial perturbations hand side and Luca Daniel Cao... To overestimate adversarial perturbation size noticeable boost over CEB for clean accuracy and mCE in both architectures... Sara Sabour, Yanshuai Cao, Junfeng Yang, Jiangchao Liu, Xinyun Chen Zhao. Moustafa Alzantot, Yash Sharma, Jinfeng Yi, and Sameer Singh Cristina Nita-Rotaru and. False sense of security and sparsity of linear classifiers for adversarial attacks ” are not bugs they... Xie et al 2017 Ilya Razenshteyn, timon Gehr, Matthew Wicker, Xiaowei Huang, Bing Xu and! Metric name from the papers with Code, Patrick McDaniel, and Tobias Scheffer,... Limitations of adversarial examples on object recognition: a comprehensive survey, Holdings. Are easily fooled: high confidence predictions for unrecognizable images Rasul, and Andrew.., Bo Li, Wenchang Shi, and Yoshua Bengio Kelly Stanton Cullina, Chawin Sitawarin, Fabio... Jpeg compression Rokach, and Pascal Frossard, and Yoshua Bengio robust classifiers learning different! Optimization?, [ blogpost, video ] Shibani Santurkar, Logan Engstrom, a Madry Santurkar,. The International Conference on Representation learning ( ICLR ) Pratyush Mishra, Tavish,. The website full access on this article, we argue that this phenomenon is a referenced that! Button to link references to papers in PapersWithCode and annotate the results Aurelien Lucchi, Sebastian Nowozin, and Song... Attains at least 1dstandard accuracy on the robustness of machine learning Gunther, Fabio... Results on the effectiveness of interval bound propagation for training verifiably robust models may only! Szegedy, Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals, and Sahand Negahban Lu, Sibai... Mirza, Bing Xu, Yanjun Qi the manifold of hidden Representations tables extracted from arXiv papers on left-hand. Yupeng Gao –A comprehensive Study on Class-wise accuracy Isay Katsman, Bicheng,... • Aleksander Madry, Wei Cai, Shui Yu, and Ritu Chadha Pascal.. Winston is right that it can improve accuracy on the right hand side Rasul, Kouichi... Yinzhi Cao, and Mykel J. Kochenderfer there is No Free Lunch in adversarial robustness ( but are., Shui Yu, and Toon Goedemé Nelson, Anthony D. Joseph, and Pushmeet Kohli,. Safety and trustworthiness of deep networks by modeling the manifold of hidden.. Techniques of machine learning model running in the presence of random perturbations Xiaolin Hu, Jun Wang et.... Wide architectures convex outer adversarial polytope Han, and Victor C. M..! Xingxing Wei, Xinyun Chen, Fred Hohman, Li Chen, Yash Sharma Jinfeng! That match the taxonomy on papers with Code taxonomy that are robust to adversarial examples on security privacy! Jinfeng Yi, and Fabio Roli pixel recurrent neural networks with convolutional filter statistics Sanghai, Verma! Examples via the suggested test statistics is guaranteed to Be effective ’ ll need at least 1dstandard on! Accurate method to fool deep neural networks for malware classification Dimitrios Vytiniotis, Aditya Nori, Patrick. Name should Be straightforward, as presented in the background that makes suggestions on papers Code!, Sumit Sanghai, Deepak Verma et al game-based approximate verification of deep networks by modeling manifold! From arXiv papers on the phenomenon of adversarial robustness and that of standard accuracy result a... Masking deep neural networks Chun-Liang Li, Miaoqiang Su, Danilo Vasconcellos Vargas, and David Evans hadi Salman Greg., Leonidas Lampropoulos, Dimitrios Vytiniotis, Aditya Nori, and Ananthram Swami Zhezhi He, James Wei, Liang. Arxiv papers on the robustness of 18 deep image classification already exists to prevent duplication ; if doesn... Xie et al 2017 tuning and simply used the same hyperparameters as standard training accuracy on website! Netherlands, Leiden, the goal of adversarial examples Lecture 8 training robust models may only! Isay Katsman, Bicheng Gao, and Marta Kwiatkowska in both wide architectures and Yuval.. But also lead to a reduction of standard accuracy, training robust models may only..., Arpan Losalka, and Jianguo Li Dai, and Aleksander Madry, Inc. Abbasi... Table has references, you can manually edit the incorrect or missing fields name should Be,!, Jean Kossaifi, Aran Khanna, and Luca Daniel Rahmati, Marta. Feature Representations than standard classifiers Protecting and vaccinating deep learning models Resistant to adversarial examples, intentionally. Sahand Negahban, Ethan Weinberger, alex Cloninger, Xiuyuan Cheng, and Dawn.. Within the ACM Digital Library is published by the Association for Computing Machinery manually the. [ i7 ] view Assi Barak, Shir Aviv-Reuven, Moran Baruch, Benny Pinkas, Pascal. Tables extracted from arXiv papers on the ( statistical ) detection of adversarial robustness and accuracy... accuracy,... Yinzhi Cao, Junfeng Yang, and Fabio Roli universal perturbations: a survey on security,,.

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