adversarial robustness as a prior for learned representations

Finally, Chapter 5 returns to some of the bigger picture questions from this Chapter, and more: here we discuss the value of adversarial robustness beyond the typical “security” justifications; instead, we consider advesarial robustness in the context of regularization, generalization, and the meaningfulness of the learned representations. Accepted at the ICLR 2019 SafeML Workshop. Running the notebooks. We find that adversarially robust source models almost always outperform their standard counterparts in terms of accuracy on the target task. I am a research engineer in the Autonomous Systems Group working on robustness in deep learning. (All models are available for download via our code/model release, and more details on our training procedure can be found there and in our paper.) • Title: Adversarial Robustness as a Prior for Learned Representations Authors: Logan Engstrom , Andrew Ilyas , Shibani Santurkar , Dimitris Tsipras , Brandon Tran , Aleksander Madry (Submitted on 3 Jun 2019 ( v1 ), last revised 27 Sep 2019 (this version, v2)) In this work, we study two different approaches for defending against black-box patch attacks. In our paper, we study this phenomenon in more detail. Logan Engstrom Adversarial machine learning and instrumental variables for flexible causal modeling, Newly discovered principle reveals how adversarial training can perform robust deep learning, Are all samples created equal? More broadly, the results we observe indicate that we still do not yet fully understand (even empirically) the ingredients that make transfer learning successful. One can thus view adversarial robustness as a very potent prior for obtaining representations that are more aligned with human perception beyond the standard goals of security and reliability. By carefully sampling examples for metric learning, our learned representation not only increases robustness, but also detects previously unseen adversarial samples. In combination with adversarial training, later works [ 21 , 36 , 61 , 55 ] achieve improved robustness by regularizing the feature representations with additional loss, which can be viewed as adding additional tasks. We conduct an empirical analysis of deep representations under the state-of-the-art attack method called PGD, and find that the attack causes the internal representation to shift closer to the "false" class. Read Paper                     Code & Models. CoRR abs/1906.00945 (2019) CoRR abs/1906.00945 (2019) EXPLOITING EXCESSIVE INVARIANCE CAUSED BY NORM-BOUNDED ADVERSARIAL ROBUSTNESS Jorn-Henrik Jacobsen¨ Vector Institute and University of Toronto Jens Popular as it is, representation learning raises concerns about the robustness of learned representations under adversarial … We identify the pervasive brittleness of deep networks' learned representations as a fundamental barrier to attaining this goal. Quantitative experiments show improvement of robustness In a recent collaboration with MIT, we explore adversarial robustness as a prior for improving transfer learning in computer vision. These are properties that are fundamental to any “truly human-level” representation. Our code and models for reproducing these results is available at https://git.io/robust-reps . Lectures from Microsoft researchers with live Q&A and on-demand viewing. Reinforcement Based Learning on Classification Task Could Yield Better Generalization and Adversarial Accuracy 12/08/2020 ∙ by Shashi Kant Gupta, et … Hadi Salman To add evaluation results you first need to. Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Aleksander Madry: Adversarial Robustness as a Prior for Learned Representations. For example, transfer learning allows perception models on a robot or other autonomous system to be trained on a synthetic dataset generated via a high-fidelity simulator, such as AirSim, and then refined on a small dataset collected in the real world. By carefully sampling examples for metric learning, our learned representation not only increases robustness, but also detects previously unseen adversarial samples. 3 Qualitative Analysis of Latent Representations under Adversarial Attack We begin our investigation by analyzing how the adversarial images are represented by different models. As we can see, adversarially robust models improve on the performance of their standard counterparts per architecture too, and the gap tends to increase as the network’s width increases: We also evaluate transfer learning on other downstream tasks including object detection and instance segmentation, both for which using robustness backbone models outperforms using standard models as shown in the table below: Overall, we have seen that adversarially robust models, although being less accurate on the source task than standard-trained models, can improve transfer learning on a wide range of downstream tasks. Many applications of machine learning require models that are human-aligned, i.e., that make decisions based on human-meaningful information about the input. Adversarial robustness has been initially studied solely through the lens of machine learning security, but recently a line of work studied the effect of imposing adversarial robustness as a prior on learned feature representations I am broadly interested…, Programming languages & software engineering, “Do Adversarially Robust ImageNet Models Transfer Better?”, Transfer Learning using Adversarially Robust ImageNet models, AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles. to this paper, Deep Residual Learning for Image Recognition. Finally, our work provides evidence that adversarially robust perception models transfer better, yet understanding precisely what causes this remains an open question. We can either use standard models that have high accuracy but little robustness on the source task; or we can use adversarially robust models, which are worse in terms of ImageNet accuracy but are robust and have the “nice” representational properties (see Figure 3). Evaluation of adversarial robustness is often error-prone leading to overestimation of the true robustness of models. We identify the pervasive brittleness of deep networks' learned representations as a fundamental barrier to attaining this goal. • Adversarial performance of data augmentation and adversarial training This next table summarizes the adversarial performance, where adversarial robustness is with respect to the learned perturbation set. We can, however, disincentivize models from using features that humans definitely don’t use by imposing a prior … Adversarial robustness has been initially studied solely through the lens of machine learning security, but recently a line of work studied the effect of imposing adversarial robustness as a prior on learned feature representations. ^ Adversarial Robustness as a Prior for Learned Representations, arXiv, 2019 ^ DROCC: Deep Robust One-Class Classification, ICML 2020 ^ ARAE: Adversarially Robust Training of Autoencoders Improves Novelty Detection, arXiv, 2020 It turns out that representations learned by robust models address the aforementioned shortcomings and make significant progress towards learning a high-level encoding of inputs. We hope that our work paves the way for more research initiatives to explore and understand what makes transfer learning work well. The hyperparameter \(\varepsilon\) governs the intended degree of invariance to the corresponding perturbations. Dimitris Tsipras In many such cases although test data might not be available, broad specifications about the types of perturbations (such as an unknown degree of rotation) may be known. More broadly, our results indicate adversarial robustness as a promising avenue for : These advantages include better-behaved gradients (see Figure 3), representation invertibility, and more specialized features. By carefully sampling examples for metric learning, our learned representation not only increases robustness, but also detects previously unseen adversarial samples. Set task=train-classifier to test the classification accuracy of learned representations. 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 cl… In fact, a recent study by Kornblith, Shlens, and Le finds that a higher accuracy of pretrained ImageNet models leads to better performance on a wide range of downstream classification tasks. Shibani Santurkar It is well known by now that standard neural networks are extremely vulnerable to such adversarial examples. This is reflected in the table below, in which we compare the accuracies of the best standard model and the best robust model (searching over the same set of hyperparameters and architectures): The following graph shows, for each architecture and downstream classification task, the performance of the best standard model compared to that of the best robust model. Improving the Robustness of Wasserstein Embedding by Adversarial PAC-Bayesian Learning Daizong Ding,1 Mi Zhang ,1 Xudong Pan,1 Min Yang,1 Xiangnan He2 1School of Computer Science, Fudan University 2School of Information Science and Technology, University … Ultimately, the quality of learned features stems from the priors we impose on them during training. 7, 12, 16 Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness Jan 2019 regularization for better adversarial robustness. In this work, we show that robust optimization can be re-cast as a tool for enforcing priors on the features learned by deep neural networks. Refining the ImageNet pretrained model can be done in several ways. ∙ MIT ∙ 0 ∙ share This week in AI Get the week's most popular data science and artificial intelligence VGG remains a mystery Although this experiment started because of an observation about a special characteristic of VGG nets, it did not provide an explanation for this phenomenon. In practical machine learning, it is desirable to be able to transfer learned knowledge from some “source” task to downstream “target” tasks. The performance of the pretrained model on the source tasks plays a major role in determining how well it transfers to the source tasks. , Learning Perceptually-Aligned Representations via Adversarial Robustness 06/03/2019 ∙ by Logan Engstrom, et al. Patch-based adversarial attacks introduce a perceptible but localized change to the input that induces misclassification. For instance, Figure 2 shows that a tiny perturbation (or change) of the pig image, a pretrained ImageNet classifier will mistakenly predict it as an “airliner” with very high confidence: Adversarial robustness is therefore typically enforced by replacing the standard loss objective with a robust optimization objective: This objective trains models to be robust to worse-case image perturbations within an \(\ell_2\) ball around the input. Quantitative experiments show improvement of robustness accuracy by up to 4% and detection efficiency by up to 6% according to Area Under Curve score over prior … Adversarial Robustness as a Feature Prior Unfortunately, we don’t have a way to explicitly control which features models learn (or in what way they learn them). (read more). Welcome to my page! What it Thinks is Important is Important: Robustness Transfers through Input Gradients Alvin Chan1 , Yi Tay 1, Yew-Soon Ong1,2 1Nanyang Technological University, 2 AI3, A STAR, Singapore Abstract Adversarial perturbations The Adversarial Robustness Toolbox is designed to support researchers and developers in creating novel defense techniques, as well as in deploying practical defenses of real-world AI systems. Adversarial Robustness as a Prior for Learned Representations Logan Engstrom*, Andrew Ilyas*, Shibani Santurkar*, Dimitris Tsipras*, Brandon Tran*, Aleksander Madry (2019) Blog Post, Github Adversarial Examples are not To answer this question, we trained a large number of standard and robust ImageNet models. These works have found that although these adversarially robust models tend to … By add a task After all, our goal is to learn broadly applicable features on the source dataset that can transfer to target datasets. • Browse our catalogue of tasks and access state-of-the-art solutions. The right-hand side shows CIFAR-10 images closest (in representation space using cosine similarity) to the query image on the left. Do Adversarially Robust ImageNet Models Transfer Better? Many applications of machine learning require models that are human-aligned, i.e., that make decisions based on human-meaningful information about the input. While progress has been made in defending against imperceptible attacks, it remains unclear how patch-based attacks can be resisted. Adversarial Robustness: Adversarial training improves models’ robustness against attacks, where the training data is augmented using adversarial samples [17, 35]. Andrew Ilyas We then re-cast robust optimization as a tool for enforcing human priors on the features learned … Research Engineer. In particular, these representations are approximately invertible, while allowing for direct visualization and manipulation of salient input features. This is known as transfer learning—a simple and efficient way to obtain performant machine learning models, especially when there is little training data or compute available for solving the target task. It requires a larger network capacity than standard training [ ] , so designing network architectures having a high capacity to handle the difficult adversarial … More broadly, our results indicate adversarial robustness as a promising avenue for improving learned representations. We use a non-robust self-supervised learning technique to learn image representations (i.e., BYOL; Grill et al., 2020). Based on the robustness python library. With the rapid development of deep learning and the explosive growth of unlabeled data, representation learning is becoming increasingly important. These desirable properties might suggest that robust neural networks are learning better feature representations than standard networks, which could improve the transferability of those features. The question that we would like to answer here is whether improving the ImageNet accuracy of the pretrained model is the only way to improve its transfer learning. • Get the latest machine learning methods with code. Adversarial robustness as a prior for learned representations, 2019. ImageNet accuracy likely correlates with the quality of features that a model learns, but it may not fully capture the downstream utility of those features. Researchers can use the Transfer learning is also common in many computer vision tasks, including image classification and object detection, in which a model uses some pretrained representation as an “initialization” to learn a more useful representation for the specific task in hand. training alone fails to produce robust models of code. Editor’s note: This post and its research are the collaborative efforts of our team, which includes Andrew Ilyas (PhD Student, MIT), Logan Engstrom (PhD Student, MIT), Aleksander Mądry (Professor at MIT), Ashish Kapoor (Partner Research Manager). Which models are better for transfer learning? Adversarial robustness as a prior for better transfer learning Learning web search intent representations from massive web search logs Reliability in Reinforcement Learning Getting into a conversational groove: New approach While existing work in robust deep learning has focused on small pixel-level ℓp norm-based perturbations, this may not account for perturbations encountered in several real world settings. Note that setting \(\varepsilon=0\) corresponds to standard training, while increasing ε induces robustness to increasingly large perturbations. We find that adversarially robust models outperform their standard counterparts on a variety of downstream computer vision tasks. We then transferred each model (using both the fixed-feature and full-network settings) to 12 downstream classification tasks and evaluated the performance. 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