vٛ��?��S[���L���6�a��7������w�9�T!�s�32�i� ����EUxeVު�8�˛�N�}$�e�a�-���`R�W� 8���^��+��f{�����W��֡Z]��}�}ѷY#��u�E�ʺ�ݥ�l�+S��Z����+��Y>m��M��e�^k� )�nl��ۅ��Zl������1>�����+�Ha9:k�"8!�����0��f� �*� 9V�xb�_�P��[>��~h�C0-�+d#��zA��̆Ӱi�c{�����ǽU,�0� Confidence-calibrated adversarial training tackles this problem by biasing the network towards low-confidence predictions on adversarial examples. While adaptive attacks designed for a particular defense are a way out of this, there are only approximate guidelines on how to perform them. %� Restricted Threat Model Attacks [requires Attacks] ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors Besides, a single attack algorithm could be insufficient to explore the space of perturbations. The increase in computational power and available data has fueled a wide deployment of deep learning in production environments. 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 Adversarial Initialization -- when your network performs the way I want. Towards Robustness against Unsuspicious Adversarial Examples. Poison Frogs! Browse our catalogue of tasks and access state-of-the-art solutions. In this paper, we introduce adversarial distributional training (ADT), a novel framework for learn-ing robust models. Get the latest machine learning methods with code. black-box attack. Adversarial Robustness Against the Union of Multiple Perturbation Models Adversarial Robustness Against the Union of Multiple Perturbation Models (Supplementary Material) A. Steepest descent and projections for ℓ∞, ℓ2, and ℓ1 adversaries In this section, we describe the steepest descent and projec- %PDF-1.5 •��3�B�H�������.w��\��`���V�c��W� �KSG'y{X[)L A Unified Benchmark for Backdoor and Data Poisoning Attacks . The most common reason is to cause a malfunction in a machine learning model. Python 1 13 0 0 Updated Jul 20, 2020. robust_overfitting Python 9 59 1 0 Updated Jul 15, 2020. qpth A fast and differentiable QP solver for PyTorch. ART provides tools that enable developers and researchers to defend and evaluate Machine Learning models and applications against the adversarial threats of Evasion, Poisoning, Extraction, and Inference. Adversarial Training and Regularization: Train with known adversarial samples to build resilience and robustness against malicious inputs. The research will be based on IBMs Adversarial Robustness 360 (ART) toolbox, an open-source library for adversarial machine learning – it’s essentially a weapon for the good-guys with state-of-the-art tools to defend and verify AI models against adversarial attacks. Ivan Evtimov U. of Washington Work done while at Facebook AI. >�6�Q�T:�5u�M��a�� 8�� ��**&hl����8��8���jT1�ͪ��Y"��z���"��=�M`����� Tv�4Y��jTTP����g��@�U�̚z��W���0�3L��a�����=|`�`@y���k��� C*��Kd�����C �mvۍ�q�5�cb_�vx(N��ׇ-� Neuron sensitivity is measured by neuron behavior variation intensity against benign and adversarial examples, and has been used to depict adversarial robustness for deep models . 22 Jun 2020 • Cassidy Laidlaw • Sahil Singla • Soheil Feizi. Create a new method. Schott et al. . This can also be seen as a form of regularization, which penalizes the norm of input gradients and makes the prediction function of the classifier smoother (increasing the input margin). Defenses against adversarial examples, such as adversarial training, are typically tailored to a single perturbation type (e.g., small $\ell_\infty$-noise). Related Events (a corresponding poster, oral, or spotlight). Such adversarial examples can, for instance, be derived from regular inputs by introducing minor—yet carefullyselected—perturbations. Adversarial training is an intuitive defense method against adversarial samples, which attempts to improve the robustness of a neural network by training it with adversarial samples. Moreover, adaptive evaluations are highly customized for particular models, which makes it difficult to compare different defenses. Lecture 11 (10/6): DL Robustness: Adversarial Poisoning Attacks and Defenses Video: Click here Readings: Clean-Label Backdoor Attacks. �6~� To the best of our knowledge, this is the first study to examine automated detection of large-scale crowdturf-ing activity, and the first to evaluate adversarial attacks against machine learning models in … Common approaches are to preprocess the inputs of a DNN, to augment the training data with adversarial examples, or to change the DNN architecture to prevent adversarial signals from propagating through the internal representation layers. 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. Final version and video presentation to be released soon! Our objective is to provide general training algorithms that can be used to train deep neural networks to be robust against natural variation in data. On ImageNet, this drastic reduction means millions of fewer model queries, rendering AutoZOOM an efficient and practical tool for evaluating adversarial robustness of AI models with limited access. Transferability refers to the ability of an adversarial example to remain effective even for the models … 02/08/2019 ∙ by Kathrin Grosse, et al. In this work, we show that it is indeed possible to adversarially train a robust model against a union of norm-bounded attacks, by using a natural generalization of the standard PGD-based procedure for adversarial training to multiple threat models. Threat model refers to the types of potential attacks considered by an approach, e.g. Evaluation of adversarial robustness is often error-prone leading to overestimation of the true robustness of models. For other perturbations, these defenses offer no guarantees and, at times, even increase the model's vulnerability. hcP�nW����`ܗm�Z�]��|�G=� 8�xkE�A�yA�.�E��V��@+�a�Q���d�2 �Ħ >.7�(\�2,G��Xˆ�N �d�c]�N$� ~�6u�Ƚm�fM�6��^��������.2�ש���9�M� a_�T�G}��V���9�6�uul��*诳m �o� C�#�U� �J}cB+��vE� �EY�?s�"DccHy��������4%��ma���� ��o�HtĚ�ľI� (�.ҹـ��?�q�:^�'q4�I{���nh��[�62~���6�|$�_�N���#���2-. We first define the notations Recent studies have identified the lack of robustness in current AI models against adversarial examples—intentionally manipulated prediction-evasive … This web page contains materials to accompany the NeurIPS 2018 tutorial, "Adversarial Robustness: Theory and Practice", by Zico Kolter and Aleksander Madry. In this paper, we propose a paradigm shift from perturbation-based adversarial robustness toward model-based robust deep learning. Adversarial Robustness Toolbox: A Python library for ML Security. Provable adversarial robustness at ImageNet scale Python 179 ... [ICML'20] Multi Steepest Descent (MSD) for robustness against the union of multiple perturbation models. standard adversarial training can not apply because it “overfits” to a particular norm. stream However, especially for complex datasets, adversarial training incurs a significant loss in accuracy and is known to generalize poorly to stronger attacks, e.g., larger perturbations or other threat models. 05/08/2020 ∙ by Liang Tong, et al. MODEL METRIC NAME METRIC VALUE GLOBAL RANK REMOVE; Add a task × Add: Not in the list? Anti-adversarial machine learning defenses start to take root Adversarial attacks are one of the greatest threats to the integrity of the emerging AI-centric economy. Thus, we try to explore the sensitivity of both critical attacking neurons and neurons outside the route. 93 0 obj Tip: you can also follow us on Twitter Researchers can use the Adversarial Robustness Toolbox to benchmark novel defenses against … %PDF-1.7 However, robustness does not generalize to larger perturbations or threat models not seen during training. Confidence-calibrated adversarial training tackles this problem by biasing the network towards low-confidence predictions on adversarial examples. label of the adversarial image is irrelevant, as long as it is not the correct label. Get Started. Threat Models Precisely defining threat models is fundamental to per-form adversarial robustness evaluations. 2019 Poster: Adversarial camera stickers: A physical camera-based attack on deep learning systems » Wed Jun 12th 01:30 -- 04:00 AM Room Pacific Ballroom More from the Same Authors. For other perturbations, these defenses offer no guarantees and, at times, even increase the model’s vulnera-bility. Part of Proceedings of the International Conference on Machine Learning 1 pre-proceedings (ICML 2020) Bibtex » Metadata » Paper » Supplemental » Authors. 0v �(Kb��E�*`�ln6bhQi"6�9�1h{E�hM�hK��_fpT� O��#�yT��PS�#�&�&��� �m۵F����ݞ�.��eO��;5s���yk3/��L_���������^V�. Just How Toxic is Data Poisoning? Adversarial Robustness Against the Union of Multiple Perturbation Models Algorithm 1 Multi steepest descent for learning classifiers that are simultaneously robust to ℓp attacks for p ∈ S Input: classifier fθ, data x, labels y Parameters: ǫp,αp for p ∈ S, maximum iterations T, loss function ℓ Adversarial patch attacks are among one of the most practical threat models against real-world computer vision systems. Browse our catalogue of tasks and access state-of-the-art solutions. "(�'I��E$e�x���ByY�Y��T��bQ�u�w4L�-�B�i�� o���W���]ь!۟vAѤ\�ʎKK^V��=[rUt*�=�m�< }���@�G2�� E�J��JasU���ʸ�q����~�@Yk����x[e�� 2�`���Z�AԑƋ��u^[�v��dHQ��� "�oߐF: �4�w�� 9��ε�"�5r��Hzn�T�}�6D�� ��+2:� Machine learning models are known to lack robustness against inputs crafted by an adversary. << /Filter /FlateDecode /Length 6187 >> May 2020: Final version and video presentation to be released soon! r�6���G�Y^� �ۻY�R"\fE)?6=��&@��en�d%3Bp�f)RSψA5�������uA��4��DPs�� .K�V�� �C�e��Y��,�;�m׷��������z�:A���̦���߾��C���Y��oC�5Q�=硌w-/��\?3�f�Du&0�}[�ơ�ĆA Statistically, robustness can be be at odds with accuracy when no assumptions are made on the data distri-bution (Tsipras et al., 2019). �~� ReLU): New method full name (e.g. While most work has defended against a single type of attack, recent work has looked at defending against multiple perturbation models using simple aggregations of multiple attacks. Xx' 5�f�c7S�z�;�P��5Ё� 2�E5w`����p0gr7U�P����/�E��Oɢ)uqS����t�Q �H��-r����e����#�(&�N(�B�:�O. 02/08/2019 ∙ by Kathrin Grosse, et al. In this work, we show that it is indeed possible to adversarially train a robust model against a union of norm-bounded attacks, by using a natural generalization of the standard PGD-based procedure for adversarial training to multiple threat models. to multiple populations, thus allowing it to maintain both accuracy and robustness in our tests. Moreover, even if a model is robust against the union of several restrictive threat models, it is still susceptible to other imperceptible adversarial examples that are not contained in any of the constituent threat models. May 2020: Preprint released for Why and when should you pool? Adversarial training yields robust models against a specific threat model. Adversarial Robustness Against the Union of Multiple Threat Models. Our paper Adversarial Robustness Against the Union of Multiple Perturbation Models was accepted at ICML 2020. The increase in computational power and available data has fueled a wide deployment of deep learning in production environments. Prominent areas of multimodal machine learning include image captioning [Chen2015coco, Krishna2017visualgenome] and visual question answering (VQA) [Antol2015vqa, Hudson2019gqa].Other multimodal tasks require a different kind of multimodal reasoning: the ability … ∙ 0 ∙ share . Because the LPIPS threat model is very broad, we find that Perceptual Adversarial Training (PAT) against a perceptual attack gives robustness against many other types of adversarial attacks. In this work, we show that it is indeed possible to adversarially train a robust model against a union of norm-bounded attacks, by using a natural generalization of the standard PGD-based procedure for adversarial training to multiple threat models. Features. Pratyush Maini, Eric Wong, Zico Kolter. ∙ Carnegie Mellon University ∙ 0 ∙ share . Prof. Zico Kolter, 2019. Adversarial Robustness against the Union of Multiple Perturbation Models. Model hardening. Get the latest machine learning methods with code. We hope the Adversarial Robustness Toolbox project will stimulate research and development around adversarial robustness of DNNs, and advance the deployment of secure AI in real world applications. %%Invocation: gs -sDEVICE=pdfwrite -dNOPAUSE -dQUIET -dBATCH -dFirstPage=1 -dLastPage=11 -sOutputFile=? Despite their successes, deep architectures are still poorly understood and costly to train. New method name (e.g. %�쏢 Next, we study alternative threat models for the adversarial example, such as the Wasserstein threat model and the union of multiple threat models. However, robustness does not generalize to larger perturbations or threat models not seen during training. May 2020: Our paper Adversarial Robustness Against the Union of Multiple Perturbation Models was accepted at ICML 2020. Adversarial Robustness Against the Union of Multiple Threat Models. ADT is formulated as a minimax optimization problem, where ? Download PDF Abstract: Owing to the susceptibility of deep learning systems to adversarial attacks, there has been a great deal of work in developing (both empirically and certifiably) robust classifiers. Adversarial Robustness Against the Union of Multiple Perturbation Models Pratyush Maini1 Eric Wong2 J. Zico Kolter3 4 Abstract Owing to the susceptibility of deep learning sys-tems to adversarial attacks, there has been a great deal of work in developing (both … New task name: Top-level area: Parent task (if any): Description (optional): Submit Remove a task × Add a method × Add: Not in the list? Evaluation of adversarial robustness is often error-prone leading to overestimation of the true robustness of models. [ICML'20]Adversarial Robustness Against the Union of Multiple Threat Models [ICML'20]Second-Order Provable Defenses against Adversarial Attacks [ICML'20]Understanding and Mitigating the Tradeoff between Robustness and Accuracy [ICML'20]Adversarial Robustness via Runtime Masking and Cleansing ;��f��}Ksh����I�-�)�q���d��V��'���[+���/?��F�9h 'x�;��@�II9��Y�Z�~h���p�� In the context of adversarial attacks, ,To study the effectiveness and limitation of disagreement ,diversity powered ensemble methods against adversarial ,examples, we argue that it is important to articulate and ,differentiate black box, grey box or white box threat models ,under offline attack scenario and online attack scenario. ∙ 6 ∙ share . See blog post here. � �By�����a]����'��;y��a�]���h�Y wu� �Y��)�h��%�q��� ��NF��7�.װ�-����U]��_n ����Z�R��U��ǼY I�ߊ�x�7��E���{����O��c�..A�^����Õ־���0���T5�N8�2E�Z#�(>�O�M{e$ ��_W��P�Nln��X�"tAkl�⼆�n��.��a��T��S�3�S��w�.2 g3�i`(�� *��-���{�ro�~/�M�w���Q�%�`a�4Cec?s���s�g� ����燡���ܲ�*���›��|�zW������#���Џ���Nf$I�J�����b#�Y@ė%�&�HJP�߽B�ӱ[a&̏` We currently implement multiple Lp-bounded attacks (L1, L2, Linf) as well as rotation-translation attacks, for both MNIST and CIFAR10. Adversarial training is an intuitive defense method against adversarial samples, which attempts to improve the robustness of a neural network by training it with adversarial samples. In contrast, high-skill workers were favoured since they voted against the union. x��!B��8�A�FZٖW+��{"� �1�z ����o^���:6�e#(u�P(def�]��O��4��Ûg��v���ﮟ�x���O�Ȯ�oV*4~�F���~����v���r��xy�du��/.ux����SӖy�p��۬(��/�]΍_ʢ*�Y���Ӿ-�{�'���T������������?S r�R�0�� �o������`��[?�?��h�ae�~���g�9Y�^��bъ�@�����z+��W�X��q�!�6�����/�� !w&�̬�V������WQ�rW HR� I0K[�C��:��/q�#�x���.�0!���*/��[�")�i�P�B�Y�mF$R�}��O����?�M[�V�DD���(���a����%�����rˍ�Ts����|us�P�u�Z�XG^��2G�7yVovdfZ��J � One of the most important questions is how to trade off adversarial robustness against natural accuracy. Our work studies the scalability and effectiveness of adversarial training for achieving robustness against a combination of multiple types of adversarial examples. <> Adversarial Initialization -- when your network performs the way I want. ... by using a natural generalization of the standard PGD-based procedure for adversarial training to multiple threat models. stream Adversarial Robustness Against the Union of Multiple Perturbation Models Author: Pratyush Maini, Eric Wong, J. Zico Kolter Subject: Proceedings of the International Conference on Machine Learning 2020 Keywords: adversarial examples, adversarial training, robust, perturbation, Machine Learning, ICML Created Date: 6/30/2020 3:22:11 AM Join the Conversation. Extended Support . This paper studies certified and empirical defenses against patch attacks. ness of deep-learning models, many fundamental questions remain unresolved. We begin with a set of experiments showing that most existing defenses, which work by pre-processing input images to mitigate adversarial patches, are easily broken by simple white-box adversaries.