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# improving adversarial robustness requires revisiting misclassified examples

Improving Adversarial Robustness Requires Revisiting Misclassified Examples. Google Scholar; Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille. The study on improving the robustness of deep neural networks against adversarial examples grows rapidly in recent years. In ICLR, 2020. Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by imperceptible perturbations. If nothing happens, download GitHub Desktop and try again. As far as the authors know, this is the first time that such reason is proposed as the underlying cause for AEs. Quanquan Gu [0] ICLR, 2020. 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. Some of the strategies aim at detecting whether an input image is adversarial or not (e.g., [17,12,13,35,16,6]). 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 In International Conference on Learning Representations. In this paper, we investigate the distinctive influence of misclassified and correctly classified examples on the final robustness of adversarial training. 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. Improving Adversarial Robustness Requires Revisiting Misclassified Examples. Work fast with our official CLI. Adversarial training is often formulated as a min-max optimization problem, with the inner maximization for generating adversarial examples. ... scraping images off the web, whereas gathering labeled examples requires hiring human labelers. Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma and Quanquan Gu. 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 Stochastic Neural Networks (SNNs) that inject noise into their hidden layers have recently been shown to achieve strong robustness against adversarial attacks. The study on improving the robustness of deep neural networks against adversarial examples grows rapidly in recent years. … In International Conference on Learning Representations, 2020. effective methods for improving robustness of neural networks. On the Convergence and Robustness of Adversarial Training. No code available yet. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. 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. ; RobustBench: json stats: various plots based on the jsons from model_info (robustness over venues, robustness vs accuracy, etc). Experimental results show that MART and its variant could significantly improve … If nothing happens, download the GitHub extension for Visual Studio and try again. Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma and Quanquan Gu. Cited by: 14 | Bibtex | Views 49 | Links. We also propose a semi-supervised extension of MART, which can leverage the unlabeled data to further improve the 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. 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. 文章目录概主要内容符号MARTWang Y, Zou D, Yi J, et al. EI. 11、Adversarial Example Detection and Classification with Asymmetrical Adversarial Training 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. We host all the notebooks at Google Colab: RobustBench: quick start: a quick tutorial to get started that illustrates the main features of RobustBench. Improving adversarial robustness requires revisiting misclassified examples. 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. (2020) 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. Yisen Wang (王奕森) [0] Difan Zou [0] Jinfeng Yi (易津锋) [0] James Bailey [0] Xingjun Ma. they're used to log you in. However, it is conservative or even pessimistic so that it sometimes hurts the natural generalization. 8、Improving Adversarial Robustness Requires Revisiting Misclassified Examples. 501, pp. works’ robustness to adversarial attacks. Learn more. Both approaches are simple – we emphasize the point that large unlabeled datasets can help bridge the gap between natural and adversarial generalization. Y Wang, X Ma, J Bailey, J Yi, B Zhou, Q Gu . 2018. You can always update your selection by clicking Cookie Preferences at the bottom of the page. Notebooks. ICML 2019, 2019. Cat: Customized adversarial training for improved robustness. ICCV 2019, 2019. 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. International Conference on Learning Representations (2018). 25 Sep 2019 (modified: 11 Mar 2020) ICLR 2020 Conference Blind Submission Readers: Everyone. In this paper, we raise a fundamental question—do we have to trade off natural generalization for adversarial robustness? GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Y Wang, X Ma, Z Chen, Y Luo, J Yi, J Bailey. 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 Improving Adversarial Robustness Requires Revisiting Misclassified Examples. Experimental results show that MART and its variant could significantly improve the state-of-the-art adversarial robustness. Code for ICLR2020 "Improving Adversarial Robustness Requires Revisiting Misclassified Examples". If nothing happens, download Xcode and try again. 9、Adversarial Policies: Attacking Deep Reinforcement Learning. 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 We use essential cookies to perform essential website functions, e.g. Improving Adversarial Robustness Requires Revisiting Misclassified Examples. 50: 2019: Improving adversarial robustness requires revisiting misclassified examples. CoRR, abs/2002.06789, 2020. Keywords: Robustness Adversarial Defense Adversarial Training. Mark. Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu. Are Labels Required for Improving Adversarial Robustness? Improving Adversarial Robustness Requires Revisiting Misclassified Examples[C]. We now consider two algorithms to study this question. 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. Learn more. Get the latest machine learning methods with code. 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. 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? Available here. 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. Powered by the Xia Li @ ZERO Lab, Full Text. Improving Adversarial Robustness Requires Revisiting Misclassified Examples. 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. 10、Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions. @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} } … Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by imperceptible perturbations. 86.46%: 56.03% ☑ WideResNet-28-10: NeurIPS 2019: 12 Proceedings of the Eighth International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, 2020. Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by imperceptible perturbations. 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. Proceedings of the Eighth International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia. 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. 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 … Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. 182-192, 2019. ‪Assistant Professor, School of EECS, Peking University‬ - ‪Cited by 931‬ - ‪Machine Learning‬ - ‪Deep Learning‬ - ‪Adversarial Learning‬ - ‪Graph Learning‬ Mitigating adversarial effects through randomization. Browse our catalogue of tasks and access state-of-the-art solutions. Adversarial training based on the minimax formulation is necessary for obtaining adversarial robustness of trained models. & Tan Y. Detecting adversarial examples via prediction difference for deep neural networks. For more information, see our Privacy Statement. 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. 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. You signed in with another tab or window. 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). Part of the code is based on the following repo. 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. In ICLR, 2020. However, it often suffers from poor generalization on both clean and perturbed data. 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. ; Feel free to suggest a new notebook based on the Model Zoo or the jsons from model_info. @article{wang2020improving,title={Improving Adversarial Robustness Re Adversarial training is often formulated as a min-max optimization problem, with the inner maximization for generating adversarial examples. 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, Y Wang, D Zou, J Yi, J Bailey, X Ma, Q Gu. international conference on learning representations, 2020. Learn more. Specifically, we find that misclassified examples indeed have a significant impact on the final robustness. [28] Minhao Cheng, Qi Lei, Pin-Yu Chen, Inderjit S. Dhillon, and Cho-Jui Hsieh. Information Sciences, vol. Peking University. Use Git or checkout with SVN using the web URL. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. This is the first time that such reason is proposed as the know. 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