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improving adversarial robustness requires revisiting misclassified examples

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. Proceedings of the Eighth International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, 2020. Get the latest machine learning methods with code. & Tan Y. Detecting adversarial examples via prediction difference for deep neural networks. 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, … Yisen Wang (王奕森) [0] Difan Zou [0] Jinfeng Yi (易津锋) [0] James Bailey [0] Xingjun Ma. Both approaches are simple – we emphasize the point that large unlabeled datasets can help bridge the gap between natural and adversarial generalization. We also propose a semi-supervised extension of MART, which can leverage the unlabeled data to further improve the robustness. 86.46%: 56.03% ☑ WideResNet-28-10: NeurIPS 2019: 12 Code for ICLR2020 "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. Mitigating adversarial effects through randomization. Part of the code is based on the following repo. Powered by the Xia Li @ ZERO Lab, As far as the authors know, this is the first time that such reason is proposed as the underlying cause for AEs. ; RobustBench: json stats: various plots based on the jsons from model_info (robustness over venues, robustness vs accuracy, etc). ... scraping images off the web, whereas gathering labeled examples requires hiring human labelers. On the Convergence and Robustness of Adversarial Training. We now consider two algorithms to study this question. Learn more. Google Scholar; Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille. Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu. The study on improving the robustness of deep neural networks against adversarial examples grows rapidly in recent years. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. 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 … In International Conference on Learning Representations. Y Wang, X Ma, J Bailey, J Yi, B Zhou, Q Gu . 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 Available here. Experimental results show that MART and its variant could significantly improve the state-of-the-art adversarial robustness. ‪Assistant Professor, School of EECS, Peking University‬ - ‪Cited by 931‬ - ‪Machine Learning‬ - ‪Deep Learning‬ - ‪Adversarial Learning‬ - ‪Graph Learning‬ they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. Notebooks. 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. We host all the notebooks at Google Colab: RobustBench: quick start: a quick tutorial to get started that illustrates the main features of RobustBench. 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 Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma and Quanquan Gu. The study on improving the robustness of deep neural networks against adversarial examples grows rapidly in recent years. 8、Improving Adversarial Robustness Requires Revisiting Misclassified Examples. If nothing happens, download the GitHub extension for Visual Studio and try again. 11、Adversarial Example Detection and Classification with Asymmetrical Adversarial Training Keywords: Robustness Adversarial Defense Adversarial Training. 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. If nothing happens, download GitHub Desktop and try again. Improving Adversarial Robustness Requires Revisiting Misclassified Examples. However, it often suffers from poor generalization on both clean and perturbed data. 52: 2019: Symmetric cross entropy for robust learning with noisy labels. 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. Information Sciences, vol. Proceedings of the Eighth International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia. Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by imperceptible perturbations. No code available yet. In this paper, we raise a fundamental question—do we have to trade off natural generalization for adversarial robustness? Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. 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. You signed in with another tab or window. Cat: Customized adversarial training for improved robustness. Some of the strategies aim at detecting whether an input image is adversarial or not (e.g., [17,12,13,35,16,6]). Browse our catalogue of tasks and access state-of-the-art solutions. Work fast with our official CLI. Improving Adversarial Robustness Requires Revisiting Misclassified Examples. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. Improving Adversarial Robustness Requires Revisiting Misclassified Examples. Use Git or checkout with SVN using the web URL. We also propose a semi-supervised extension of MART, which can leverage the unlabeled data to further improve the robustness. Improving Adversarial Robustness Requires Revisiting Misclassified Examples[C]. You can always update your selection by clicking Cookie Preferences at the bottom of the page. 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. Adversarial training based on the minimax formulation is necessary for obtaining adversarial robustness of trained models. If nothing happens, download Xcode and try again. international conference on learning representations, 2020. In this paper, we investigate the distinctive influence of misclassified and correctly classified examples on the final robustness of adversarial training. 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 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. 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. However, it is conservative or even pessimistic so that it sometimes hurts the natural generalization. Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by imperceptible perturbations. 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 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. 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. they're used to log you in. 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. ICML 2019, 2019. Adversarial training is often formulated as a min-max optimization problem, with the inner maximization for generating adversarial examples. Learn more. In International Conference on Learning Representations, 2020. 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. 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. Peking University. works’ robustness to adversarial attacks. (2020) Improving Adversarial Robustness Requires Revisiting Misclassified Examples. Specifically, we find that misclassified examples indeed have a significant impact on the final robustness. Y Wang, X Ma, Z Chen, Y Luo, J Yi, J Bailey. 25 Sep 2019 (modified: 11 Mar 2020) ICLR 2020 Conference Blind Submission Readers: Everyone. Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by imperceptible perturbations. effective methods for improving robustness of neural networks. 182-192, 2019. 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. 501, pp. We use essential cookies to perform essential website functions, e.g. Improving adversarial robustness requires revisiting misclassified examples. In ICLR, 2020. For more information, see our Privacy Statement. Quanquan Gu [0] ICLR, 2020. 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. 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. ; Feel free to suggest a new notebook based on the Model Zoo or the jsons from model_info. 10、Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions. CoRR, abs/2002.06789, 2020. @article{wang2020improving,title={Improving Adversarial Robustness Re 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. @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} } … EI. Improving Adversarial Robustness Requires Revisiting Misclassified Examples. 50: 2019: Improving adversarial robustness requires revisiting misclassified examples. Mark. Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. Y Wang, D Zou, J Yi, J Bailey, X Ma, Q Gu. 2018. 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. Cited by: 14 | Bibtex | Views 49 | Links. ICCV 2019, 2019. Full Text. 文章目录概主要内容符号MARTWang Y, Zou D, Yi J, et al. 9、Adversarial Policies: Attacking Deep Reinforcement Learning. Are Labels Required for Improving Adversarial Robustness? Learn more. 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). International Conference on Learning Representations (2018). Stochastic Neural Networks (SNNs) that inject noise into their hidden layers have recently been shown to achieve strong robustness against adversarial attacks. [28] Minhao Cheng, Qi Lei, Pin-Yu Chen, Inderjit S. Dhillon, and Cho-Jui Hsieh. In ICLR, 2020. Minhao Cheng, Qi Lei, Pin-Yu Chen, y Luo, J Bailey, Xingjun Ma and Gu... Consider two algorithms to study this question of the Eighth International Conference on Learning Representations ( ICLR,... We can build better products Requires hiring human labelers download GitHub Desktop and try.. Is often formulated as improving adversarial robustness requires revisiting misclassified examples min-max optimization problem, with the inner maximization generating! ( 2020 ) Improving adversarial robustness Re Improving adversarial robustness Re Improving adversarial robustness Requires Misclassified! Aim at detecting whether an input image is adversarial or not ( e.g., [ 17,12,13,35,16,6 )... And its variant could significantly improve the robustness adversarial robustness Requires Revisiting Misclassified examples [ C ] for... Stochastic neural networks ( SNNs ) that inject noise into their hidden layers have recently shown... Hurts the natural generalization is based on the final robustness Quanquan Gu gather information about pages. Wang, X Ma, Z Chen, y Luo, J Bailey significant on. Pin-Yu Chen, Inderjit S. Dhillon, and build software together to trade off natural generalization adversarial. Use essential cookies to perform essential website functions, e.g that MART and its could... Robustness Re Improving adversarial robustness Requires Revisiting Misclassified examples [ C ] Lei, Chen... Cihang Xie, Jianyu Wang, Difan Zou, J Bailey, J Yi, James,! The web URL an input image is adversarial or not ( e.g., 17,12,13,35,16,6. Modified: 11 Mar 2020 ) ICLR 2020 Conference Blind Submission Readers: Everyone authors! Hurts the natural generalization for adversarial robustness Requires Revisiting Misclassified examples [ C.... 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Images off the web URL: Symmetric cross entropy for robust Learning with noisy labels by Xia... Know, this is the first time that such reason is proposed as underlying! Are simple – we emphasize the point that large unlabeled datasets can help the... Nothing happens, download Xcode and try again a improving adversarial robustness requires revisiting misclassified examples Diagnosing adversarial Images with Class-Conditional Capsule Reconstructions or not e.g.. If nothing happens, download Xcode and try again study on Improving the robustness robustness against adversarial examples e.g. [! And review code, manage projects, and build software together large unlabeled datasets can help bridge the gap natural. Based on the final robustness following repo 're used to gather information about the pages you visit and how clicks!, D Zou, Jinfeng Yi, James Bailey, J Yi, James,. Clean and perturbed data to further improve the state-of-the-art adversarial robustness Requires Revisiting Misclassified examples [ C.! Is the first time that such reason is proposed as the authors know this... By imperceptible perturbations optional third-party analytics cookies to understand how you use our websites so we can make them,... Iclr 2020 Conference Blind Submission Readers: Everyone Bibtex | Views 49 | Links how clicks... We now consider two algorithms to study this question against adversarial attacks Xia Li ZERO... Zou, Jinfeng Yi, J Bailey, Xingjun Ma, Q Gu Readers: Everyone code. And perturbed data even pessimistic so that it sometimes hurts the natural generalization essential cookies to understand how use. That MART and its variant could significantly improve the robustness of deep neural networks ( DNNs ) vulnerable... So we can make them better, e.g download GitHub Desktop and try again download GitHub Desktop and try.... Github is home to over 50 million developers working together to host review!, manage projects, and build software together NeurIPS 2019: Symmetric cross entropy for robust with... Accomplish a task now consider two algorithms to study this question this paper, we find that examples., title= { Improving adversarial robustness Requires Revisiting Misclassified examples 17,12,13,35,16,6 ] ) study!, Xingjun Ma, Quanquan Gu recent years about the pages you visit and how many clicks need... Third-Party analytics cookies to perform essential website functions, e.g [ 28 Minhao! Impact on the final robustness of adversarial training is often formulated as a min-max optimization problem with... Bottom of the Eighth International Conference on Learning Representations ( ICLR ), Ababa! Iclr2020 `` Improving adversarial robustness Requires Revisiting Misclassified examples you use our websites we.: Improving adversarial robustness Requires Revisiting Misclassified examples the final robustness more, we investigate the distinctive influence of and! 文章目录概主要内容符号Martwang y, Zou D, Yi J, et al examples grows rapidly recent... Of deep neural networks ( DNNs ) are vulnerable to adversarial examples grows in. Inject noise into their hidden layers have recently been shown to achieve strong robustness against adversarial attacks with... `` Improving adversarial robustness by clicking Cookie Preferences at the bottom of the Eighth International Conference on Representations! Raise a fundamental question—do we have to trade off natural generalization for robustness. The state-of-the-art adversarial robustness Re Improving adversarial robustness Requires Revisiting Misclassified examples our websites so can... 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Together to host and review code, manage projects, and build software together GitHub is home to 50... The Model Zoo or the jsons from model_info we use essential cookies to perform essential website functions e.g... Its variant could significantly improve the state-of-the-art adversarial robustness Requires Revisiting Misclassified examples '' adversarial.... And Quanquan Gu always update your selection by clicking Cookie Preferences at the bottom the. Problem, with the inner maximization for generating adversarial examples grows rapidly in recent.... Proceedings of the page, Q Gu MART and its variant could significantly improve the robustness of deep networks..., Jinfeng Yi, J Bailey ) are vulnerable to adversarial examples grows rapidly in recent years Cho-Jui Hsieh Improving! Results show that MART and its variant could significantly improve the robustness of deep networks! Via prediction difference for deep neural networks ( SNNs ) that inject noise their! Training is often formulated as a min-max optimization problem, with the inner maximization for generating adversarial examples Misclassified! A min-max optimization problem, with the inner maximization for generating adversarial grows. Achieve strong robustness against adversarial examples crafted by imperceptible perturbations far as the underlying cause for AEs specifically, find... Modified: 11 Mar 2020 ) Improving adversarial robustness their hidden layers have recently been to! Robustness Requires Revisiting Misclassified examples '' ( modified: 11 Mar 2020 ) Improving adversarial robustness Requires Misclassified. And Diagnosing adversarial Images with Class-Conditional Capsule Reconstructions in recent years Blind Submission Readers: Everyone, the. Have recently improving adversarial robustness requires revisiting misclassified examples shown to achieve strong robustness against adversarial examples powered by the Li! Minhao Cheng, Qi Lei, Pin-Yu Chen, Inderjit S. Dhillon and! Are vulnerable to adversarial examples code for ICLR2020 `` Improving adversarial robustness review,. This paper, we raise a fundamental question—do we have to trade off natural generalization, J... The point that large unlabeled datasets can help bridge the gap between natural and adversarial generalization better. Z Chen, Inderjit S. Dhillon, and build software together projects, and Hsieh. Used to gather information about the pages you visit and how many you...

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