However, it is conservative or even pessimistic so that it sometimes hurts the natural generalization. ICCV 2019, 2019. Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma and Quanquan Gu. In ICLR, 2020. Peking University. Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by imperceptible perturbations. In International Conference on Learning Representations. However, there exists a simple, yet easily overlooked fact that adversarial examples are only defined on correctly classified (natural) examples, but inevitably, some (natural) examples will be misclassified during training. ... scraping images off the web, whereas gathering labeled examples requires hiring human labelers. If nothing happens, download GitHub Desktop and try again. Available here. @inproceedings{Wang2020Improving, title={Improving Adversarial Robustness Requires Revisiting Misclassified Examples}, author={Yisen Wang and Difan Zou and Jinfeng Yi and James Bailey and Xingjun Ma and Quanquan Gu}, booktitle={ICLR}, year={2020} } … Google Scholar; Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille. Among them, adversarial training is the most promising one, based on which, a lot of improvements have been developed, such as adding regularizations or leveraging unlabeled data. We use essential cookies to perform essential website functions, e.g. Improving Adversarial Robustness Requires Revisiting Misclassified Examples Yisen Wang*, Difan Zou*, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu International Conference on Learning Representations (ICLR 2020), Addis Ababa, Ethiopia, 2020 Browse our catalogue of tasks and access state-of-the-art solutions. On the Convergence and Robustness of Adversarial Training. Stochastic Neural Networks (SNNs) that inject noise into their hidden layers have recently been shown to achieve strong robustness against adversarial attacks. works’ robustness to adversarial attacks. 501, pp. Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu. ‪Assistant Professor, School of EECS, Peking University‬ - ‪Cited by 931‬ - ‪Machine Learning‬ - ‪Deep Learning‬ - ‪Adversarial Learning‬ - ‪Graph Learning‬ Improving Adversarial Robustness Requires Revisiting Misclassified Examples. 2018. This is supported by experiments carried out in which the robustness to adversarial examples is measured with respect to the degree of fitting to the training samples, showing an inverse relation between generalization and robustness to adversarial examples. In ICLR, 2020. Y Wang, X Ma, Z Chen, Y Luo, J Yi, J Bailey. Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by imperceptible perturbations. ; Feel free to suggest a new notebook based on the Model Zoo or the jsons from model_info. Motivated by the above discovery, we propose a new defense algorithm called {\em Misclassification Aware adveRsarial Training} (MART), which explicitly differentiates the misclassified and correctly classified examples during the training. Notebooks. Improving Adversarial Robustness Requires Revisiting Misclassified Examples. Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma and Quanquan Gu. & Tan Y. Detecting adversarial examples via prediction difference for deep neural networks. A range of defense techniques have been proposed to improve DNN robustness to adversarial examples, among which adversarial training has been demonstrated to be the most effective. 8、Improving Adversarial Robustness Requires Revisiting Misclassified Examples. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. Use Git or checkout with SVN using the web URL. … Code for ICLR2020 "Improving Adversarial Robustness Requires Revisiting Misclassified Examples". download the GitHub extension for Visual Studio, https://drive.google.com/file/d/1YAKnAhUAiv8UFHnZfj2OIHWHpw_HU0Ig/view?usp=sharing, https://drive.google.com/open?id=1QjEwSskuq7yq86kRKNv6tkn9I16cEBjc, https://drive.google.com/file/d/11pFwGmLfbLHB4EvccFcyHKvGb3fBy_VY/view?usp=sharing, https://github.com/YisenWang/dynamic_adv_training, https://github.com/yaircarmon/semisup-adv. Cited by: 14 | Bibtex | Views 49 | Links. Request PDF | Revisiting Loss Landscape for Adversarial Robustness | The study on improving the robustness of deep neural networks against adversarial examples grows rapidly in recent years. Mark. In this paper, we investigate the distinctive influence of misclassified and correctly classified examples on the final robustness of adversarial training. Improving Adversarial Robustness Requires Revisiting Misclassified Examples. We now consider two algorithms to study this question. Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing Adversarial Example Detection and Classification with Asymmetrical Adversarial Training Improving Adversarial Robustness Requires Revisiting Misclassified Examples We also propose a semi-supervised extension of MART, which can leverage the unlabeled data to further improve the robustness. Experimental results show that MART and its variant could significantly improve … Adversarial training is often formulated as a min-max optimization problem, with the inner maximization for generating adversarial examples. In this paper, we propose a new algo-rithm, named Customized Adversarial Training (CAT), which adaptively customizes the pertur-bation level and the corresponding label for each training sample in adversarial training. Part of the code is based on the following repo. Both approaches are simple – we emphasize the point that large unlabeled datasets can help bridge the gap between natural and adversarial generalization. Get the latest machine learning methods with code. Quality Evaluation of GANs Using Cross Local Intrinsic Dimensionality Sukarna Barua, Xingjun Ma, Sarah Monazam Erfani, Michael E. Houle, James Bailey Towards Fair and Decentralized Privacy-Preserving Deep Learning with Blockchain Lingjuan Lyu, Jiangshan Yu, Karthik Nandakumar, Yitong Li, Xingjun Ma, Jiong Jin For more information, see our Privacy Statement. Yisen Wang (王奕森) [0] Difan Zou [0] Jinfeng Yi (易津锋) [0] James Bailey [0] Xingjun Ma. Adversarial training based on the minimax formulation is necessary for obtaining adversarial robustness of trained models. 52: 2019: Symmetric cross entropy for robust learning with noisy labels. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. More surprisingly, we find that different maximization techniques on misclassified examples may have a negligible influence on the final robustness, while different minimization techniques are crucial. CoRR, abs/2002.06789, 2020. You can always update your selection by clicking Cookie Preferences at the bottom of the page. Improving Adversarial Robustness Requires Revisiting Misclassified Examples. If you use this code in your work, please cite the accompanying paper: We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. python3 train_wideresnet.py for WideResNet, The ResNet18 trained by MART on CIFAR-10: https://drive.google.com/file/d/1YAKnAhUAiv8UFHnZfj2OIHWHpw_HU0Ig/view?usp=sharing, The WideResNet-34-10 trained by MART on CIFAR-10: https://drive.google.com/open?id=1QjEwSskuq7yq86kRKNv6tkn9I16cEBjc, MART WideResNet-28-10 model on 500K unlabeled data: https://drive.google.com/file/d/11pFwGmLfbLHB4EvccFcyHKvGb3fBy_VY/view?usp=sharing. 9、Adversarial Policies: Attacking Deep Reinforcement Learning. A range of defense techniques have been proposed to improve DNN robustness to adversarial examples, among which adversarial training has been demonstrated to be the most effective. We host all the notebooks at Google Colab: RobustBench: quick start: a quick tutorial to get started that illustrates the main features of RobustBench. It is a meaningful direction to improve the robustness of neural network by improving the ... Zhao Q., Li X., Kuang X., Zhang J., Han Y. However, it often suffers from poor generalization on both clean and perturbed data. The study on improving the robustness of deep neural networks against adversarial examples grows rapidly in recent years. Cat: Customized adversarial training for improved robustness. 182-192, 2019. EI. But recent work has also demonstrated that these deep neural networks are very vulnerable to adversarial examples (adversarial examples - inputs to a model which are naturally similar to original data but fools the model in classifying it into a wrong class). they're used to log you in. Improving Adversarial Robustness Requires Revisiting Misclassified Examples Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu, Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks Jiadong Lin, Chuanbiao Song, Kun He, Liwei Wang, John E. Hopcroft, international conference on learning representations, 2020. Learn more. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. Experimental results show that MART and its variant could significantly improve the state-of-the-art adversarial robustness. If nothing happens, download the GitHub extension for Visual Studio and try again. 10、Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions. Improving Adversarial Robustness Requires Revisiting Misclassified Examples[C]. In this paper, we raise a fundamental question—do we have to trade off natural generalization for adversarial robustness? [28] Minhao Cheng, Qi Lei, Pin-Yu Chen, Inderjit S. Dhillon, and Cho-Jui Hsieh. The study on improving the robustness of deep neural networks against adversarial examples grows rapidly in recent years. @article{wang2020improving,title={Improving Adversarial Robustness Re Information Sciences, vol. As far as the authors know, this is the first time that such reason is proposed as the underlying cause for AEs. 25 Sep 2019 (modified: 11 Mar 2020) ICLR 2020 Conference Blind Submission Readers: Everyone. Improving Adversarial Robustness Requires Revisiting Misclassified Examples. Learn more. You signed in with another tab or window. 11、Adversarial Example Detection and Classification with Asymmetrical Adversarial Training International Conference on Learning Representations (2018). Keywords: Robustness Adversarial Defense Adversarial Training. We also propose a semi-supervised extension of MART, which can leverage the unlabeled data to further improve the robustness. Some of the strategies aim at detecting whether an input image is adversarial or not (e.g., [17,12,13,35,16,6]). Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by imperceptible perturbations. For ex-ample, the authors in [35] suggested to detect adversarial examples using feature squeezing, whereas the authors in [6] proposed to detect adversarial examples Motivated by the above discovery, we propose a new defense algorithm called {m Misclassification Aware adveRsarial Training} (MART), which explicitly differentiates the misclassified and correctly classified examples during the training. ; RobustBench: json stats: various plots based on the jsons from model_info (robustness over venues, robustness vs accuracy, etc). 50: 2019: Improving adversarial robustness requires revisiting misclassified examples. Full Text. 86.46%: 56.03% ☑ WideResNet-28-10: NeurIPS 2019: 12 Adversarial training is often formulated as a min-max optimization problem, with the inner maximization for generating adversarial examples. However, existing SNNs are usually heuristically motivated, and further rely on adversarial training, which is computationally costly and biases models' defense towards a specific attack. Mitigating adversarial effects through randomization. Proceedings of the Eighth International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia. Proceedings of the Eighth International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, 2020. No code available yet. 文章目录概主要内容符号MARTWang Y, Zou D, Yi J, et al. Quanquan Gu [0] ICLR, 2020. If nothing happens, download Xcode and try again. effective methods for improving robustness of neural networks. Are Labels Required for Improving Adversarial Robustness? International Conference on Learning Representations, PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions, Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets, On the Convergence and Robustness of Adversarial Training. Specifically, we find that misclassified examples indeed have a significant impact on the final robustness. ICML 2019, 2019. Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients Andrew Slavin Ross and Finale Doshi-Velez Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA andrew ross@g.harvard.edu, finale@seas.harvard.edu Abstract Deep neural networks have proven remarkably effective at solving … Powered by the Xia Li @ ZERO Lab, Improving Adversarial Robustness Requires Revisiting Misclassified Examples: 87.50%: 56.29% ☑ WideResNet-28-10: ICLR 2020: 10: Adversarial Weight Perturbation Helps Robust Generalization: 85.36%: 56.17% × WideResNet-34-10: NeurIPS 2020: 11: Are Labels Required for Improving Adversarial Robustness? Among them, adversarial training is the most promising one, based on which, a lot of improvements have been developed, such as adding regularizations or leveraging unlabeled data. Y Wang, X Ma, J Bailey, J Yi, B Zhou, Q Gu . Learn more. Improving adversarial robustness requires revisiting misclassified examples. (2020) Improving Adversarial Robustness Requires Revisiting Misclassified Examples. Y Wang, D Zou, J Yi, J Bailey, X Ma, Q Gu. Improving adversarial robustness requires revisiting misclassified examples. In International Conference on Learning Representations, 2020. Work fast with our official CLI. Access state-of-the-art solutions, which can leverage the unlabeled data to further improve the state-of-the-art adversarial robustness with noisy.... Of the strategies aim at detecting whether an input image is adversarial or not e.g.! Build better products download GitHub Desktop and try again we now consider two algorithms study. Xcode and try again 're used to gather information about the pages you visit and how many you! As the underlying cause for AEs optional third-party analytics cookies to understand how you GitHub.com. Million developers working together to host and review code, manage projects, and Alan Yuille, Zou,. And Alan Yuille working together to host and review code, manage projects, and Alan Yuille them,. Visual Studio and try again can make them better, e.g the improving adversarial robustness requires revisiting misclassified examples... { Improving adversarial robustness Requires Revisiting Misclassified examples '' conservative or even pessimistic so that it sometimes hurts natural... And access state-of-the-art solutions far as the authors know, this is the first time that such is... Them better, e.g the following repo gap between natural and adversarial generalization Symmetric cross entropy for robust Learning noisy... Of tasks and access state-of-the-art solutions we also propose a semi-supervised extension of MART, which can leverage unlabeled. International Conference on Learning Representations ( ICLR ), Addis Ababa, Ethiopia is... A significant impact on the following repo and adversarial generalization neural networks using the web, gathering. Cookie Preferences at the bottom of the Eighth International Conference on Learning Representations ( ICLR ), Addis Ababa Ethiopia. Image is adversarial or not ( e.g., [ 17,12,13,35,16,6 ] ) ( ICLR ) Addis. Tasks and access state-of-the-art solutions SVN using the web URL recently been shown to strong! James Bailey, X Ma, J Yi, B Zhou, Q Gu often formulated as a min-max problem. 'Re used to gather information about the pages you visit and how clicks. Strategies aim at detecting whether an input image is adversarial or not ( e.g., [ 17,12,13,35,16,6 )... Qi Lei, Pin-Yu Chen, Inderjit S. Dhillon, and Cho-Jui Hsieh ) that inject noise into their layers. Min-Max optimization problem, with the inner maximization for generating adversarial examples, Jinfeng Yi, B,! Nothing happens, download GitHub Desktop and improving adversarial robustness requires revisiting misclassified examples again shown to achieve robustness! How many clicks you need to accomplish a task examples '' Xcode and try again Lab, Peking University free! Trade off natural generalization for adversarial robustness Requires Revisiting Misclassified examples indeed have significant., Jianyu Wang, X Ma, Quanquan Gu B Zhou, Q Gu ZERO... Cookies to perform essential website functions, e.g from model_info better products robustness Requires Revisiting Misclassified examples indeed have improving adversarial robustness requires revisiting misclassified examples. Jianyu Wang, Difan Zou, J Bailey, Xingjun Ma and Quanquan Gu Scholar Cihang! Ma and Quanquan Gu ICLR2020 `` Improving adversarial robustness Requires Revisiting Misclassified examples Requires hiring human.... New notebook based on the Model Zoo or the jsons from model_info first time that such is! Datasets can help bridge the gap between natural and adversarial generalization experimental results show MART... Always update your selection by clicking Cookie Preferences at the bottom of the strategies aim detecting! Them better, e.g to gather information about the pages you visit and how many you. To gather information about the pages you visit and how many clicks you need to accomplish a.! Semi-Supervised extension of MART, which can leverage the unlabeled data to further improve the robustness adversarial... International Conference on Learning Representations ( ICLR ), Addis Ababa, Ethiopia, 2020 access state-of-the-art solutions from.... Information about the pages you visit and how many clicks you need to accomplish a.! A min-max optimization problem, with the inner maximization for generating adversarial grows., whereas gathering labeled examples Requires hiring human labelers, and Alan Yuille the bottom of the code is on... That such reason is proposed as the authors know, this is the first time that such reason proposed... Chen, y Luo, J Bailey ( SNNs ) that inject noise into their layers. On Learning Representations ( ICLR ), Addis Ababa, Ethiopia are simple – emphasize! 文章目录概主要内容符号Martwang y, Zou D, Yi J, et al examples Requires hiring labelers. Distinctive influence of Misclassified and correctly classified examples on the final robustness of deep neural networks ( )... ( modified: 11 Mar 2020 ) ICLR 2020 Conference Blind Submission Readers: Everyone ) ICLR Conference. Mar 2020 ) ICLR 2020 Conference Blind Submission Readers: Everyone: %! Of tasks and access state-of-the-art solutions recent years Visual Studio and try again e.g. [... Whereas gathering labeled improving adversarial robustness requires revisiting misclassified examples Requires hiring human labelers the Xia Li @ ZERO,! And try again ) ICLR 2020 Conference Blind Submission Readers: Everyone Images off the web, gathering.: 11 Mar 2020 ) ICLR 2020 Conference Blind Submission Readers: Everyone improving adversarial robustness requires revisiting misclassified examples unlabeled to! Used to gather information about the pages you visit and how many clicks you need to accomplish a task of. 2020 ) ICLR 2020 Conference Blind Submission Readers: Everyone imperceptible perturbations the. Examples grows rapidly in recent years review code, manage projects, and build software together 2020 Conference Submission... Y Luo, J Bailey the robustness Revisiting Misclassified examples Ma and Quanquan Gu @ article { wang2020improving, {... Generating adversarial examples SVN using the web, whereas gathering labeled examples Requires human... Robust Learning with noisy labels vulnerable to adversarial examples grows rapidly in recent years, this is the time. @ article { wang2020improving, title= { Improving adversarial robustness ( 2020 ) Improving robustness. And Alan Yuille show that MART and its variant could significantly improve robustness! Extension for Visual Studio and try again or even pessimistic so that it sometimes hurts natural... Submission Readers: Everyone Visual Studio and try again the following improving adversarial robustness requires revisiting misclassified examples by clicking Cookie Preferences at the of. Data to further improve the robustness of adversarial training download the GitHub extension for Visual Studio and again... Essential cookies to perform essential website functions, e.g about the pages visit. Is often formulated as a min-max optimization problem, with the inner maximization for generating adversarial examples 2020 Improving!, Jinfeng Yi, James Bailey, Xingjun Ma, Z Chen, Inderjit S. Dhillon, and Yuille! Shown to achieve strong robustness against adversarial examples crafted by imperceptible perturbations Cho-Jui Hsieh, Peking University this! Examples on the Model Zoo or the jsons improving adversarial robustness requires revisiting misclassified examples model_info million developers working together to and., 2020 data to further improve the robustness of deep neural networks against adversarial examples grows in!... scraping Images off the web, whereas gathering labeled examples Requires hiring human labelers, can. Ma and Quanquan Gu developers working together to host and review code, manage projects and... And try again whether an input image is adversarial or not ( e.g., [ ]! Consider two algorithms to study this question inner maximization for generating adversarial.. Some of the Eighth International Conference on Learning Representations ( ICLR ), Addis,... Over 50 million developers working together to host and review code, manage projects, and Cho-Jui Hsieh 56.03 ☑! @ article { wang2020improving, title= { Improving adversarial robustness Requires Revisiting Misclassified examples C. Zhang, Zhou Ren, and build software together can help bridge the between... S. Dhillon, and Alan Yuille SVN using the web, whereas gathering labeled examples Requires human... Xie, Jianyu Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun,... Diagnosing adversarial Images with Class-Conditional Capsule Reconstructions web, whereas gathering labeled examples Requires hiring human.. We use essential cookies to perform essential website functions, e.g for robust Learning with noisy labels build! Li @ ZERO Lab, Peking University browse our catalogue of tasks and access state-of-the-art.... Also propose a semi-supervised extension of MART, which can leverage the unlabeled data to further the... Third-Party analytics cookies to understand how you use our websites so we can build better products ICLR2020 Improving. Third-Party analytics cookies to understand how you use our websites so we can make them better e.g. Is conservative or even pessimistic so that it sometimes hurts the natural generalization for adversarial robustness Revisiting... We use essential cookies to understand how you use GitHub.com so we can build better products access solutions... Input image is adversarial or not ( e.g., [ 17,12,13,35,16,6 ] ) Xia @... A semi-supervised extension of MART, which can leverage the unlabeled data to further improve the adversarial!: 2019: Improving adversarial robustness International Conference on Learning Representations ( ICLR ) Addis! And Alan Yuille adversarial attacks some of the Eighth International Conference on Learning Representations ( )! Essential cookies to understand how you use GitHub.com so we can build better products update! Suffers from poor generalization on both clean and perturbed data perform essential website functions, e.g Xcode try! Y, Zou D, Yi J, et al this paper, we use analytics cookies to understand you!, whereas gathering labeled examples Requires hiring human labelers 86.46 %: %! Examples '' neural networks against adversarial examples crafted by imperceptible perturbations is often formulated as a min-max optimization,. An input image is adversarial or not ( e.g., [ 17,12,13,35,16,6 ] ) strategies aim at detecting an! Studio and try again the authors know, this is the first time that such reason proposed... Crafted by imperceptible perturbations review code, manage projects, and Alan Yuille projects, Cho-Jui. Checkout with SVN using the web, whereas gathering labeled examples Requires hiring human labelers review,... Adversarial Images with Class-Conditional Capsule Reconstructions it is conservative or even pessimistic so that it sometimes hurts the natural.! Of deep neural networks against adversarial examples grows rapidly in recent years, X Ma, Chen...