Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy Philipp Benz , Chaoning Zhang , Adil Karjauv , In So Kweon PDF Cite arXiv We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. How Does Batch Normalization Help Optimization?, [blogpost, video] Shibani Santurkar, Dimitris Tsipras ). Robustness may be at odds with accuracy. If nothing happens, download Xcode and try again. • We show, by conducting extensive experiments, that such a trade-off holds across various settings, including attack/defense methods, model architectures, datasets, etc. Evaluation of adversarial robustness is often error-prone leading to overestimation of the true robustness of models. Code for "Robustness May Be at Odds with Accuracy" - MadryLab/robust-features-code GitHub is where the world builds software Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development Towards a Principled Science of Deep Learning. • is how to trade off adversarial robustness against natural accuracy. As Bengio et al. Key topics include: generalization, over Improving the mechanisms by which NN decisions are understood is an important direction for both establishing trust in sensitive domains and learning more about the stimuli to which NNs respond. Adversarial Robustness May Be at Odds With Simplicity 01/02/2019 ∙ by Preetum Nakkiran, et al. Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors. • • We are interested in both experimental and theoretical approaches that advance our understanding. We present both theoretical and empirical analyses that connect the adversarial robustness of a model to the number of tasks that it is trained on. For more information, see our Privacy Statement. ICLR 2019. ICLR 2019. You can always update your selection by clicking Cookie Preferences at the bottom of the page. Learn more. robustness.datasets module Module containing all the supported datasets, which are subclasses of the abstract class robustness.datasets.DataSet. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. We prove that (i) if the dataset is separated, then there always exists a robust and accurate classifier, and (ii) this classifier can be obtained by rounding a locally Lipschitz function. To add evaluation results you first need to. Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy Philipp Benz , Chaoning Zhang , Adil Karjauv , In So Kweon PDF Cite arXiv Robustness tests were originally introduced to avoid problems in interlaboratory studies and to identify the potentially responsible factors [2]. • We find that the adversarial robustness of a DNN is at odds with the backdoor robustness. Title:Adversarial Robustness May Be at Odds With Simplicity Authors:Preetum Nakkiran Abstract: Current techniques in machine learning are so far are unable to learn classifiers that are robust to adversarial perturbations. Andrew Ilyas*, Logan Engstrom*, Ludwig Schmidt, and Aleksander Mądry. These differences, in particular, seem to result in unexpected benefits: the representations learned by robust models tend to align better with salient data characteristics and human perception. • Bengio et al. By default the code looks for this directory in the environmental variable, Train your own robust restricted ImageNet models (via, Produce adversarial examples and visualize gradients, with example code in, Reproduce the ImageNet examples seen in the paper (via. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. all 7, Deep Residual Learning for Image Recognition. Dimitris Tsipras*, Shibani Santurkar*, Logan Engstrom*, Alexander Turner, Aleksander Madry Google Scholar Robustness May Be at Odds with Accuracy Dimitris Tsipras*, Shibani Santurkar*, Logan Engstrom*, Alexander Turner, Aleksander Mądry ICLR 2019 How Does Batch Normalization Help Optimization? Robustness May Be at Odds with Accuracy Dimitris Tsipras*, Shibani Santurkar*, Logan Engstrom*, Alexander Turner, Aleksander Madry ICLR 2019 How Does Batch Normalization Help Optimization? Harvard Machine Learning Theory We are a research group focused on building towards a theory of modern machine learning. Robustness May Be at Odds with Accuracy Dimitris Tsipras* MIT tsipras@mit.edu Shibani Santurkar* MIT shibani@mit.edu Logan Engstrom* MIT engstrom@mit.edu Alexander Turner MIT turneram@mit.edu Aleksander Madry˛ MIT madry@mit.edu Abstract We they're used to log you in. While adaptive attacks designed for a particular defense are a way out of this, there are only approximate guidelines on how to perform them. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy… add a task Code for "Robustness May Be at Odds with Accuracy". This means that a robustness test was performed at a late stage in the method validation since interlaboratory studies are In International Conference on Learning Representations, 2019. We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations … Alexander Turner 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. The silver lining: adversarial training induces more semantically meaningful gradients and gives adversarial examples with GAN-like trajectories: This repository comes with (after following the instructions) three restricted ImageNet pretrained models: You will need to set the model ckpt directory in the various scripts/ipynb files where appropriate if you want to complete any nontrivial tasks. As another example, decision trees or sparse linear models enjoy global interpretability, however their expressivity may be limited [1, 23]. If nothing happens, download GitHub Desktop and try again. Aleksander Madry, We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. ∙ Harvard University ∙ 0 ∙ share Current techniques in machine learning are so far are unable to learn classifiers that are robust to adversarial perturbations. We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. Is robustness the cost of accuracy? はじめに Robustness May Be at Odds with Accuracyを読んだのでメモ. 気持ち この論文ではadversarial robustnessとstandard accuracy(例えば画像分類の精度など)が両立しないことを示し,それはrobust modelとstandard model… We use essential cookies to perform essential website functions, e.g. We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists in a fairly simple and natural setting. These findings also corroborate a similar phenomenon observed empirically in more complex settings. – a comprehensive study on the robustness of 18 deep image classification models. ICLR 2019 Robustness May Be at Odds with Accuracy Intriguing Properties of Neural Networks Explaining and Harnessing Adversarial Examples Lecture 8 Readings In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning Robustness May Be at Odds with Accuracy We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. .. arXiv.org, abs/1808.01688, 2018. Use Git or checkout with SVN using the web URL. Home Anxiety Depression Diseases Disability Medicine Exercise Fitness Equipment Health & Fitness Back Pain Acne Beauty Health Care Dental Care Critical Care Skin Care Supplements Build Muscle Nutrition Weight Loss Popular Diets Physical Therapy Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry. We show that adversarial robustness often inevitablely results in accuracy loss. •Robustness: Accuracy on adversarial examples •To boost performance on clean data, we propose to add perturbation in the feature space instead of pixel space Robustness may be at odds with accuracy. Robustness May Be at Odds with Accuracy, Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Mądry. Learn more. to this paper, See Work fast with our official CLI. [] D. Tsipras, S. Santurkar, L. Engstrom, A. Turner, and A. Madry. Logan Engstrom* These findings also corroborate a similar phenomenon observed empirically in more complex settings. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. Robustness may be at odds with Currently supported datasets: ImageNet (robustness.datasets.ImageNet) RestrictedImageNet CIFAR-10 ) Evaluating Logistic Regression Models in R. GitHub Gist: instantly share code, notes, and snippets. Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. Dismiss Grow your team on GitHub GitHub is home to over 50 million developers working together. I am currently a third-year Ph.D. student of Electrical and Computer Engineering (DICE) at VITA, The University of Texas at Austin, advised by Dr. Zhangyang (Atlas) Wang. This repository provides code for both training and using the restricted robust resnet models from the paper: Robustness May Be at Odds with Accuracy If nothing happens, download the GitHub extension for Visual Studio and try again. Robustness often leads to lower test accuracy, which is undesirable. Logan Engstrom Follow their code on GitHub. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy... We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. Statistically, robustness can be be at odds with accuracy when no assumptions are made on the data distri-bution (Tsipras et al., 2019). Moreover, adaptive evaluations are highly customized for particular models, which makes it difficult to compare different defenses. https://arxiv.org/abs/1805.12152. On the ImageNet classification task, we demonstrate a network with an accuracy-robustness area (ARA) of 0.0053, an ARA 2.4 times greater than the previous state-of-the-art value. ICLR 2019. Learn more. Nevertheless, robustness is desirable in some scenarios where humans are involved in the loop. ICLR (2019). they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. This has led to an empirical line of work on adversarial download the GitHub extension for Visual Studio, Get a downloaded version of the ImageNet training set. Dimitris Tsipras Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers. Join them to grow your own development Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers. • (read more). In the meantime, non-robust features also matter for accuracy, and it seems unwise to discard them as in adversarial training. Although deep networks achieve strong accuracy on a range of computer vision benchmarks, they remain vulnerable to adversarial attacks, where imperceptible input perturbations fool the network. Robustness May Be at Odds with Accuracy. Shibani Santurkar Madry Lab has 29 repositories available. Get the latest machine learning methods with code. For example, it is shown by [29] that adversarial robustness may be at odds with accuracy. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. My We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists in a fairly simple and natural setting. Browse our catalogue of tasks and access state-of-the-art solutions. 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