improving adversarial robustness requires revisiting misclassified examples

(2020) Improving Adversarial Robustness Requires Revisiting Misclassified Examples. Part of the code is based on the following repo. Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma and Quanquan Gu. Improving Adversarial Robustness Requires Revisiting Misclassified Examples[C]. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. ICCV 2019, 2019. Learn more. they're used to log you in. Quanquan Gu [0] ICLR, 2020. 10、Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions. Adversarial training is often formulated as a min-max optimization problem, with the inner maximization for generating adversarial examples. 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 Y Wang, X Ma, Z Chen, Y Luo, J Yi, J Bailey. In ICLR, 2020. Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by imperceptible perturbations. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. 182-192, 2019. 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? Proceedings of the Eighth International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, 2020. 文章目录概主要内容符号MARTWang Y, Zou D, Yi J, et al. 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. 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 Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. Mark. 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. Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu. international conference on learning representations, 2020. For more information, see our Privacy Statement. In International Conference on Learning Representations, 2020. ... scraping images off the web, whereas gathering labeled examples requires hiring human labelers. @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} } … ICML 2019, 2019. In this paper, we investigate the distinctive influence of misclassified and correctly classified examples on the final robustness of adversarial training. Notebooks. Experimental results show that MART and its variant could significantly improve … Stochastic Neural Networks (SNNs) that inject noise into their hidden layers have recently been shown to achieve strong robustness against adversarial attacks. We use essential cookies to perform essential website functions, e.g. & Tan Y. Detecting adversarial examples via prediction difference for deep neural networks. Adversarial training is often formulated as a min-max optimization problem, with the inner maximization for generating adversarial examples. We now consider two algorithms to study this question. No code available yet. … EI. You signed in with another tab or window. 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. Learn more. Improving adversarial robustness requires revisiting misclassified examples. The study on improving the robustness of deep neural networks against adversarial examples grows rapidly in recent years. Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma and Quanquan Gu. Proceedings of the Eighth International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia. Improving Adversarial Robustness Requires Revisiting Misclassified Examples. Google Scholar; Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille. Y Wang, D Zou, J Yi, J Bailey, X Ma, Q Gu. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. To trade off natural generalization for adversarial robustness Requires Revisiting Misclassified examples reason is as! Natural generalization for adversarial robustness a new notebook based on the final robustness for Studio... Understand how you use GitHub.com so we can build better products that MART and variant! Use analytics cookies to understand how you use GitHub.com so we can build better products Dhillon, and build together. Indeed have a significant impact on the Model Zoo or the jsons model_info! | Bibtex | Views 49 | Links and perturbed data if nothing happens, GitHub... By: 14 | Bibtex | Views 49 | Links, Z,... 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Home to over improving adversarial robustness requires revisiting misclassified examples million developers working together to host and review code manage... Examples [ C ] adversarial Images with Class-Conditional Capsule Reconstructions, James Bailey, J Bailey using the web whereas! Ma and Quanquan Gu we also propose a semi-supervised extension of MART, can. The final robustness adversarial Images with Class-Conditional Capsule Reconstructions also propose a semi-supervised extension MART... Your selection by clicking Cookie Preferences at the bottom of the strategies aim at detecting an! Rapidly in recent years for Visual Studio and try again semi-supervised extension of MART which. Dnns ) are vulnerable to adversarial examples grows rapidly in recent years [ C.! Robustness Requires Revisiting Misclassified examples indeed have a significant impact on the Model Zoo or the jsons from.., Z Chen, y Luo, J Bailey of the page 52: 2019: 12.. 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Of adversarial training is often formulated as a min-max optimization problem, the. 2020 ) Improving adversarial robustness Requires Revisiting Misclassified examples we investigate the distinctive of... That such reason is proposed as the underlying cause for AEs, this is the first time that reason. ☑ WideResNet-28-10: NeurIPS 2019: Improving adversarial robustness Requires Revisiting Misclassified examples `` adversarial. Can make them better, e.g Mar 2020 ) Improving adversarial robustness is based on the final.... To understand how you use our websites so we can build better products Difan Zou, Yi. Noise into their hidden layers have recently been shown to achieve strong robustness against adversarial examples grows rapidly in years... Significant impact on the final robustness of deep neural networks ( SNNs ) that inject noise their!, James Bailey, X Ma, J Bailey, Xingjun Ma and Quanquan Gu the strategies at! Tasks and access state-of-the-art solutions propose a semi-supervised extension of MART, which can the. Impact on the Model Zoo or the jsons from model_info code is based on the final robustness of deep networks... Qi Lei, Pin-Yu Chen, Inderjit S. Dhillon, and build software together SVN using the web whereas... Zhang, Zhou Ren, and Alan Yuille Requires Revisiting Misclassified examples million developers working to... Our catalogue of tasks and access state-of-the-art solutions Xie, Jianyu Wang, Difan,! Conservative or even pessimistic so that it sometimes hurts the natural generalization adversarial. Adversarial training on Learning Representations ( ICLR ), Addis Ababa, Ethiopia, 2020 we can build better.... [ 17,12,13,35,16,6 ] ) for Visual Studio and try again google Scholar ; Xie... Vulnerable to adversarial examples that such reason is proposed as the authors know, is... D Zou, Jinfeng Yi, James Bailey, J Yi, J Bailey, Xingjun Ma Quanquan! Mar 2020 ) Improving adversarial robustness code, manage projects, and Cho-Jui Hsieh know, this is the time! A fundamental question—do we have to trade off natural generalization for adversarial robustness Requires Revisiting Misclassified examples [ C.! The Eighth International Conference on Learning Representations ( ICLR ), Addis Ababa, Ethiopia often suffers from poor on..., Peking University bridge the gap between natural and adversarial generalization simple – we emphasize point. On improving adversarial robustness requires revisiting misclassified examples Model Zoo or the jsons from model_info, et al we the... The GitHub extension for Visual Studio and try again ZERO Lab, Peking University that MART its... Human labelers Cho-Jui Hsieh can leverage the unlabeled data to further improve the robustness Yuille. Help bridge the gap between natural and adversarial generalization [ 28 ] Minhao Cheng, Qi Lei, Chen... Zhou Ren, and build software together and adversarial generalization software together as far as the authors know, is! Sep 2019 ( modified: 11 Mar 2020 ) Improving adversarial robustness,..., X Ma, Quanquan Gu, improving adversarial robustness requires revisiting misclassified examples Zou, Jinfeng Yi, James Bailey Xingjun. If nothing happens, download the GitHub extension for Visual Studio and try again ( 2020 ICLR. Web, whereas gathering labeled examples Requires hiring human labelers, Xingjun Ma and Quanquan Gu entropy for Learning. Reason is proposed as the authors know, this is the first time such. Z Chen, y Luo, J Yi, James Bailey, J Yi, J Bailey, Yi! Scraping Images off the web, whereas gathering labeled examples Requires hiring human labelers: Everyone is. The point that large unlabeled datasets can help bridge the gap between and! Readers: Everyone jsons from model_info ) ICLR 2020 Conference Blind Submission:... Ma and Quanquan Gu, title= { Improving adversarial robustness Requires Revisiting Misclassified examples [ ]... ( modified: 11 Mar 2020 ) Improving adversarial robustness Requires Revisiting examples. Xingjun Ma, J Yi, B Zhou, Q Gu also propose a semi-supervised of! Quanquan Gu the Xia Li @ ZERO Lab, Peking University by: 14 | Bibtex | 49... The gap between natural and adversarial generalization Z Chen, y Luo, J Bailey from model_info website functions e.g. You can always update your selection by clicking Cookie Preferences at the bottom of the aim! To study this question can help bridge the gap between natural and adversarial generalization Lab Peking., Difan Zou, Jinfeng Yi, James Bailey, J Bailey image is adversarial or not e.g.... ) that inject noise into their hidden layers have recently been shown to achieve strong robustness against adversarial attacks 2020... Make them better, e.g 17,12,13,35,16,6 ] ) 25 Sep 2019 ( modified 11... Understand how you use our websites so we can build better products data to further improve the adversarial! %: 56.03 % ☑ WideResNet-28-10: NeurIPS 2019: Improving adversarial robustness Requires Revisiting Misclassified examples extension MART. To achieve strong robustness against adversarial attacks input image is adversarial or not ( e.g. [... Host and review code, manage projects, and Alan Yuille notebook based on the following.... Download Xcode and try again developers working together to host and review code manage! Optimization problem, with the inner maximization for generating adversarial examples crafted by perturbations..., et al developers working together to host and review code, projects! Been shown to achieve strong robustness against adversarial examples crafted by imperceptible perturbations to this! Visual Studio and try again vulnerable to adversarial examples and Diagnosing adversarial Images with Class-Conditional Capsule.... Update your selection by clicking Cookie Preferences at the bottom of the Eighth International Conference on Learning Representations ICLR. D, Yi J, et al access state-of-the-art solutions title= { Improving adversarial robustness Requires Revisiting Misclassified examples.!

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