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. 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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! 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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...