Quanquan Gu [0] ICLR, 2020. 11、Adversarial Example Detection and Classification with Asymmetrical Adversarial Training The study on improving the robustness of deep neural networks against adversarial examples grows rapidly in recent years. Get the latest machine learning methods with code. 9、Adversarial Policies: Attacking Deep Reinforcement Learning. 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. 8、Improving Adversarial Robustness Requires Revisiting Misclassified Examples. Adversarial training based on the minimax formulation is necessary for obtaining adversarial robustness of trained models. 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. 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? Improving adversarial robustness requires revisiting misclassified examples. Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma and Quanquan Gu. Yisen Wang (王奕森) [0] Difan Zou [0] Jinfeng Yi (易津锋) [0] James Bailey [0] Xingjun Ma. Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. 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. they're used to log you in. Learn more. (2020) Improving Adversarial Robustness Requires Revisiting Misclassified Examples. If nothing happens, download Xcode and try again. 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. Browse our catalogue of tasks and access state-of-the-art solutions. Some of the strategies aim at detecting whether an input image is adversarial or not (e.g., [17,12,13,35,16,6]). In International Conference on Learning Representations. Mark. Google Scholar; Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille. 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. 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 @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} } … 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). ... scraping images off the web, whereas gathering labeled examples requires hiring human labelers. CoRR, abs/2002.06789, 2020. Use Git or checkout with SVN using the web URL. Cat: Customized adversarial training for improved robustness. 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. effective methods for improving robustness of neural networks. 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. We use essential cookies to perform essential website functions, e.g. You signed in with another tab or window. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. If nothing happens, download GitHub Desktop and try again. Learn more. Cited by: 14 | Bibtex | Views 49 | Links. 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 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 ; RobustBench: json stats: various plots based on the jsons from model_info (robustness over venues, robustness vs accuracy, etc). Y Wang, X Ma, J Bailey, J Yi, B Zhou, Q Gu . 2018. 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. 501, pp. Available here. For more information, see our Privacy Statement. We also propose a semi-supervised extension of MART, which can leverage the unlabeled data to further improve the robustness. Work fast with our official CLI. We host all the notebooks at Google Colab: RobustBench: quick start: a quick tutorial to get started that illustrates the main features of RobustBench. Proceedings of the Eighth International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia. Are Labels Required for Improving Adversarial Robustness? Specifically, we find that misclassified examples indeed have a significant impact on the final robustness. 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, You can always update your selection by clicking Cookie Preferences at the bottom of the page. 182-192, 2019. However, it often suffers from poor generalization on both clean and perturbed data. Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu. 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. 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. In this paper, we raise a fundamental question—do we have to trade off natural generalization for adversarial robustness? Adversarial training is often formulated as a min-max optimization problem, with the inner maximization for generating adversarial examples. … Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma and Quanquan Gu. international conference on learning representations, 2020. [28] Minhao Cheng, Qi Lei, Pin-Yu Chen, Inderjit S. Dhillon, and Cho-Jui Hsieh. In ICLR, 2020. Experimental results show that MART and its variant could significantly improve the state-of-the-art adversarial robustness. 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. In International Conference on Learning Representations, 2020. 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 Learn more. 文章目录概主要内容符号MARTWang Y, Zou D, Yi J, et al. 10、Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions. Both approaches are simple – we emphasize the point that large unlabeled datasets can help bridge the gap between natural and adversarial generalization. ‪Assistant Professor, School of EECS, Peking University‬ - ‪Cited by 931‬ - ‪Machine Learning‬ - ‪Deep Learning‬ - ‪Adversarial Learning‬ - ‪Graph Learning‬ 52: 2019: Symmetric cross entropy for robust learning with noisy labels. Improving adversarial robustness requires revisiting misclassified examples. Notebooks. In ICLR, 2020. 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 … Part of the code is based on the following repo. 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