Conï¬dence-Calibrated Adversarial Training 3. Mądry Lab The primary focus of our lab is the science of modern machine learning. Shibani Santurkar, Ludwig Schmidt, Aleksander Mądry. Alexander Turner, Dimitris Tsipras, Aleksander Mądry. Kai Xiao, Undergraduate Students: CIFAR10 Adversarial Examples Challenge. This web page contains materials to accompany the NeurIPS 2018 tutorial, âAdversarial Robustness: Theory and Practiceâ, by Zico Kolter and Aleksander Madry. Readme License. Most machine learning techniques were designed to work on specific problem sets in which the training and test data are generated from the same statistical distribution (). Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Mądry. Two take-home messages from the experiments on Ima-geNet: (1) training exclusively on adversarial examples results in performance degradation; and (2) simply training with adversarial examples and clean images in turn can improve network perfor- Adversarially Robust Generalization Requires More Data, A Classification-Based Study of Covariate Shift in GAN Distributions, On the Limitations of First-Order Approximation in GAN Dynamics, Towards Deep Learning Models Resistant to Adversarial Attacks, The video and notes (with example code) for the. Speciï¬cally, training robust models may not only be more resource-consuming, but also lead to â¦ We observe that when training with few samples, adversarial training has a positive effect on model generalization (especially on MNIST). Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Mądry. Identifying Statistical Bias in Dataset Replication, alyze Madry et al.’s adversarial training formulation. NeurIPS 2018 tutorial on adversarial robustness, BREEDS: Benchmarks for Subpopulation Shift, Noise or Signal: The Role of Image Backgrounds in Object Recognition. For training classifiers, we only use the training partition of each dataset. Standard CIFAR10 augmentation (+-2 pixel crops) can be achieved by setting adversarial_training: true, spatial_method: random, random_tries: 1, spatial_limits: [2, 2, 0]. Brandon Tran, Jerry Li, Aleksander Mądry. Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Mądry. [2018] explore adversarial robustness and propose PGD adversarial training, where models are trained with a minimax robust optimization objective. Recently,lotsofworks[2,10,11,18,22,33,34]fo- cus on analyzing and improving adversarial machine learn- ing. However, as training data increase, the standard accuracy of robust models drops below that of the standard model (#train= 0). Aleksandar Makelov, robustness is a package we (students in the MadryLab) created to make training, evaluating, and exploring neural networks flexible and easy. We observe improved invariance in the embedding space for models trained with FreeLB, which is positively correlated with generalization. MIT License Releases No releases published. MNIST Adversarial Examples Challenge. 2020). Adversarial training solves a min-max optimization problem, with the inner maximization generating adversarial exam- ples by maximizing the classiﬁcation loss, and the outer minimization ﬁnding model parameters by minimizing the loss on adversarial examples gen- erated from the inner maximization. We use it in almost all of our projects (whether they involve adversarial training or not!) Adversarially Robust Generalization Requires More Data, Check out our adversarial robustness challenges for. different classes, and use it to examine existing adversarial training formulations. The comparison between ResNet-152, ResNet-101, and ResNet-50 … NeurIPS 2019 (spotlight presentation). A library for experimenting with, training and evaluating neural networks, with a focus on adversarial robustness. This is further evidence that adversarial examples arise as a result of non-robust features and are not necessarily tied to the standard training â¦ Adversarial training with adversarial examples. Robustness May Be at Odds with Accuracy, Guillaume Leclerc, ICLR 2020 (oral presentation). Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks. Problems of Adversarial Training Following (Madry et al.,2018), adversarial training is given as the following min-max problem: min w E max k 1 L(f(x+ ;w);y) (1) with wbeing the classiﬁer’s parameters. [blogpost] PGD-based (Projected Gradient Descent) adversarial training (Madry et al., 2018), which enables us to perform such diversiï¬ed adversarial training on large-scale state-of-the-art models. PGD ADVERSARIAL TRAINING Adversarial example generation Madry et. How Does Batch Normalization Help Optimization?, [blogpost, video] Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. [19] proposed adversarial training (AT) that dynamically trained the model on the generated PGD attacks, achieving the ﬁrst empirical adversarial robust classiﬁer on CIFAR-10. Brandon Tran, On the other hand, works such as [30] ... decrease the loss on such adversarial samples. From ImageNet to Image Classification: Contextualizing Progress on Benchmarks, We aim to combine theoretical and empirical insights to build a principled and thorough understanding of key techniques in machine learning, such as deep learning, as well as the challenges we face in this context. We confirm in our own experiments that by training with the PGD attacker with more iterations, the resulted model exhibits a similar behavior to the case where we apply better gradients for the attacker. ICML 2019. Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, Aleksander Mądry. [blogpost] equates this with projected gradient descent (PGD) [11]. Adversarial training (AT) is among the most effec- tive techniques to improve model robustness by augmenting training data with adversarial exam- ples. Shibani Santurkar, Dimitris Tsipras, Aleksander Mądry. Perhaps the simplest strategy for training an adversarially robust model is also the one which seems most intuitive. [blogpost] By their estimations , this adversary was PGD, which they motivate as the strongest attack utilizing the local first-order information about the network. We verify that this is the case by “free training” ResNet-101 and ResNet-152 with ϵ = 4. . However, robustness does not generalize to larger perturbations or Image Synthesis with a Single (Robust) Classifier, [22] solves the maximization step by generating adversarial samples using an iterative method named Projected Gradi- Do Adversarially Robust ImageNet Models Transfer Better?, Somewhat surprisingly,we ﬁnd that a natural min-max formulation proposed by (Madry et al., 2017), under a variety of instantiations, encourages train-ingamodelthatsatisﬁes ourpropertywell. Logan Engstrom, Andrew Ilyas, Aleksander Madry, Shibani Santurkar, Brandon Tran, Dimitris Tsipras • May 6, 2019 11 minute read Read ... We then train a model on the resulting dataset without adversarial training and find that the resulting model has non-trivial accuracy and robustness! Florian Tramer, Nicholas Carlini, Wieland Brendel, Aleksander Mądry. Logan Engstrom, Andrew Ilyas, Aleksander Madry, Shibani Santurkar, Brandon Tran, Dimitris Tsipras â¢ May 6, 2019 11 minute read ... (non-adversarial) training. We copy the network architecture from madry2017towards for the MNIST task, and use a similar ResNet architecture to song2018pixeldefend for other datasets. Recently, there has been much progress on adversarial attacks against neural networks, such as the cleverhans library and the code by Carlini and Wagner.We now complement these advances by proposing an attack challenge for the MNIST dataset (we recently released a CIFAR10 variant of this challenge).We have trained a robust network, and the objective is â¦ It is very efficient and successful at creating adversarial examples for standard (non-robust) models. Learning Perceptually-Aligned Representations via Adversarial Robustness, Madry et al. [2019] improve upon this work with a modiﬁed loss function and develop a better understanding of the trade-off between adversarial accuracy and natural accuracy. Zico Kolter and Aleksander Madry It is known that increasing the number of attack iterations can create harder adversarial examples Madry et al. al, 2018 ICLR 18 Madry, Makelov, Schmidt, Tsipras, Vladu “Towards deep learning models resistant to adversarial attacks” PGD ADV. . ICLR 2020 (oral presentation). NeurIPS 2018. Here x0 N is the adversarial image at the N’th iteration and xis the clean image. In adversarial training regime, mod- els are trained with mini-batches augmented with adversar- ial samples. ICLR 2019. Andrew Ilyas, Logan Engstrom, Aleksander Mądry. ICLR 2019. NeurIPS 2020. The primary focus of our lab is the science of modern machine learning. and it will be a dependency in many of our upcoming code releases. Nur Muhammad Shafiullah, Alumni: Samarth Gupta, Calvin Lee, Madry et al. Section5performs a detailed empirical study of our method. The Madry Lab developed a defense model by focusing on training a sufficiently high-capacity network and using the strongest possible adversary. Adversarial training, which trains networks with adversarial examples, constitutes the current foundation of state-of-the-arts for defending against adver- sarial attacks [7, 18, 23, 45]. MNIST Adversarial Examples Challenge.   •, Adversarial Robustness - Theory and Practice, Chapter 3 â Adversarial examples: solving the inner maximization, Chapter 4 â Adversarial training: solving the outer minimization, Chapter 5 â Beyond adversaries [coming soon]. Shibani Santurkar, Al-though PGD is shown to be a universal ï¬rst-order adversary (Madry et al.,2018), robustness against PGD cannot confer robustness to attacks beyond the ï¬rst-order adversaries. Noise or Signal: The Role of Image Backgrounds in Object Recognition, Madry's Adversarial Training + Fine-tuning Figure 2. As we seek to deploy machine learning systems not only on virtual domains, but also in real systems, it becomes critical that we examine not only whether the systems don’t simply work “most of the time”, but which are truly robust and reliable. Adversarial training method shows promising re- sults in this direction. Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Andrew Ilyas, Aleksander Mądry. Michael Sun, Loc Trinh, Alexander Turner, Abhinav Venigalla, Tony Wang, Andy Wei, Wendy Wei, Brandon Zeng, Jeffrey Zhang. We discuss important prior work in Sec-tion6and conclude in Section7. Fast and simple methods (e.g., single-step gra- dient ascent) are used for generating adversarial samples, in order to reduce computational complexity. About. A prominent illustration of this phenomenon is adversarial training with FGSM (fast gradient sign method). Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Andrew Ilyas, Logan Engstrom, Aleksander Mądry. We are looking for motivated MIT undergraduate students who would help us build up our infrastructure for deep learning experimentation. How Does Batch Normalization Help Optimization? Certifiable distributional robustness with principled adversarial training. ICML 2020. NeurIPS 2019. A major theme in our investigations is rethinking machine learning from the perspective of security, robustness and reliability. Do Adversarially Robust ImageNet Models Transfer Better? Aman Sinha, Hongseok Namkoong, and John Duchi. NeurIPS 2020 (oral presentation). Logan Engstrom, ICLR 2018. Adversarial training was originally proposed as a ICML 2020. Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Mądry. [blogpost] Using adversarial examples’ predictions (ℐA) leads to more privacy leakage. Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Jacob Steinhardt, Aleksander Mądry. 2. The notes are in very early draft form , and we will be updating them (organizing material more, writing them in a more consistent form with the relevant citations, etc) for an official release in early 2019. . However, the adversarially trained models do not perform well enough on test data or under other attack algorithms unseen during … No packages published . On Adaptive Attacks to Adversarial Example Defenses, Kai Xiao, Logan Engstrom, Andrew Ilyas, Aleksander Mądry. [blogpost] [blogpost] Dimitris Tsipras, By using adversarial attacks as a data augmentation method, a model trained with adversarial examples achieves considerable ro- bustness. Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples---inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. [blogpost] Jerry Li, Aleksander Mądry, John Peebles, Ludwig Schmidt (alphabetic order). Adversarial Examples Are Not Bugs, They Are Features, Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability, ﻿Clean-Label Backdoor Attacks, Preliminaries As in existing work, such as (Carlini & … Spectral Signatures in Backdoor Attacks, mini-batches of training samples are contaminated with adversarial perturbations (alterations that are small and yet cause misclassiﬁcation), and then used to update network parameters until the resulting model learns to resist such attacks. [blogpost] Exploring the Landscape of Spatial Robustness, A Closer Look at Deep Policy Gradients, [blogposts: 1, 2, 3] , Madry et al. NeurIPS 2018 (oral presentation). Farzan Farnia, Jesse Zhang, and David Tse. In this paper, we introduce Randomized Adversarial Training (RAT), a technique that is efficient both against $\ell_2$ and $\ell_\infty$ attacks. ICLR 2018. [Download notes as jupyter notebook](adversarial_training.tar.gz) ## From adversarial examples to training robust models In the previous chapter, we focused on methods for solving the inner maximization problem over perturbations; that is, to finding the solution to the problem  \DeclareMathOperator*{\maximize}{maximize} \maximize_{\|\delta\| \leq \epsilon} \ell(h_\theta(x + â¦ Graduate Students: ICLR 2019. Adversarial training improves the model robustness by train-ing on adversarial examples generated by FGSM and PGD (Goodfellow et al., 2015; Madry et al., 2018). For more details about architectures, hyperparameters and adversarial training methods, please refer to Appendix C. During mini-batch training the inner maximization problem, max k k1 L(f(x+ ;w);y); (2) is approximately solved. Adversarial training, the process of training on adversarial examples generated in ∥ ⋅ ∥ p-balls around the training data, is a very natural approach to constructing robust models (Goodfellow et al. Towards deep learning models resistant to adversarial attacks. On MNIST, as of today this method is able to reach an accuracy of 88:79% for adversarial perturbations with an L The L We then present embedding objectives and algorithms for han-dling low-conﬁdence points, and end-to-end instantiations, in Section4. The notes are in very early draft form, and we will be updating them (organizing material more, writing them in a more consistent form with the relevant citations, etc) for an official release in early 2019. ,2019;Min et al. Aleksander MadryË MIT madry@mit.edu Abstract We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. A few projects using the library include: Second, the saddle point problem speciï¬es a clear goal that an ideal robust classiï¬er should Although many notions of robustness and reliability exist, one particular topic in this area that has raised a great deal of interest in recent years is that of adversarial robustness: can we develop … adversarial training, that encourages the output of the net-work to be smooth. Until then, however, we hope they are still a useful reference that can be used to explore some of the key ideas and methodology behind adversarial robustness, from standpoints of both generating adversarial attacks on classifiers and training classifiers that are inherently robust. Jupyter Notebook MIT 76 413 6 0 Updated Nov 24, 2020 label-consistent-backdoor-code Andrew Ilyas, Logan Engstrom, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Mądry. Recently, there has been much progress on adversarial attacks against neural networks, such as the cleverhans library and the code by Carlini and Wagner.We now complement these advances by proposing an attack challenge for the MNIST dataset (we recently released a CIFAR10 variant of this challenge).We have trained a robust network, and the … ). A Classification-Based Study of Covariate Shift in GAN Distributions, This web page contains materials to accompany the NeurIPS 2018 tutorial, “Adversarial Robustness: Theory and Practice”, by Zico Kolter and Aleksander Madry. Un- fortunately, the high cost of generating strong adversarial examples makes standard adversarial training impractical on … This is precisely the problem of training a robust classiï¬er using adversarial training techniques. Andrew Ilyas, Cynthia Liu, Tarek Mansour, Vivek Miglani, Aleksander Mądry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu (alphabetic order). Madry et al. Adversarial training is an effective defense method to train robust models against adversarial attacks. The most common reason is to cause a malfunction in a machine learning model. 2.4 Carlini and Wagner Carlini and Wagner introduce L 2-norm, L 1-norm, and L 0-norm targeted at- tacks [12]. Implementation Matters in Deep RL: A Case Study on PPO and TRPO, Adversarial Training. Investigating the robustness of state-of-the-art CNN architectures to simple spatial transformations. Packages 0. Madry et al. point, but new adversarial examples may also emerge in other regions due to the high non-linearity of DNNs. BREEDS: Benchmarks for Subpopulation Shift, Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors, which adversarial training is the most effective. We aim to combine theoretical and empirical insights to build a principled and thorough understanding of key techniques in machine learning, such as deep learning, as well as the challenges we face in this context. Adversarial training (Madry et al.,2017;Good-fellow et al.,2014) has been well studied in com-puter vision, but past work shows that it often hurts generalization (Raghunathan et al. Sec.3.1. Ludwig Schmidt, Adversarial machine learning is a machine learning technique that attempts to fool models by supplying deceptive input. Zhang et al. ICML 2018. NeurIPS 2018 (spotlight presentation). ICML 2018. In our notation this corresponds to training on samples drawn from X ϵ for some ϵ. Resources. On Adaptive Attacks to Adversarial Example Defenses, From ImageNet to Image Classification: Contextualizing Progress on Benchmarks, Identifying Statistical Bias in Dataset Replication, Implementation Matters in Deep RL: A Case Study on PPO and TRPO, Image Synthesis with a Single (Robust) Classifier, Learning Perceptually-Aligned Representations via Adversarial Robustness, Adversarial Examples Are Not Bugs, They Are Features, Exploring the Landscape of Spatial Robustness, Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability, Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors.