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. Secondly, a given DNN can be “hardened” to make it more robust against adversarial inputs. Confidence-calibrated adversarial training tackles this problem by biasing the network towards low-confidence predictions on adversarial examples. ∙ 6 ∙ share . 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. Evaluation of adversarial robustness is often error-prone leading to overestimation of the true robustness of models. ∙ 0 ∙ share . Browse our catalogue of tasks and access state-of-the-art solutions. 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 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. Threat model refers to the types of potential attacks considered by an approach, e.g. Model hardening. Russell Howes Facebook AI Brian Dolhansky Facebook AI Hamed Firooz Facebook AI Cristian Canton Facebook AI Abstract. ;��f��}Ksh����I�-�)�q���d��V��'���[+���/?��F�9h 'x�;��@�II9��Y�Z�~h���p�� According to [5], a threat model specifies the adversary’s goals, capabilities, and knowledge under which an attack is performed and a defense is built to be robust. 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 paper, we introduce adversarial distributional training (ADT), a novel framework for learn-ing robust models. 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 . Adversarial training yields robust models against a specific threat model. •��3�B�H�������.w��\��`���V�c��W� �KSG'y{X[)L %�쏢 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. Analyses done on multiple Text Classification tasks. A key challenge in adversarial robustness is the lack of a precise mathematical characterization of human perception, used in the very definition of adversarial attacks that are imperceptible to human eyes. No code available yet. 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 While adaptive attacks designed for a particular defense are a way out of this, there are only approximate guidelines on how to perform them. Adversarial patch attacks are among one of the most practical threat models against real-world computer vision systems. 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. Moreover, adaptive evaluations are highly customized for particular models, which makes it difficult to compare different defenses. Get the latest machine learning methods with code. Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training: 89.98%: 36.64% × WideResNet-28-10: NeurIPS 2019: 33: Adversarial Interpolation Training: A Simple Approach for Improving Model Robustness: 90.25%: 36.45% × WideResNet-28-10: OpenReview, Sep 2019: 34 black-box attack. 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� Defenses against adversarial examples, such as adversarial training, are typically tailored to a single perturbation type (e.g., small $\ell_\infty$-noise). Xx' 5�f�c7S�z�;�P��5Ё� 2�E5w`����p0gr7U�P����/�E��Oɢ)uqS����t�Q �H��-r����e����#�(&�N(�B�:�O. This web page contains materials to accompany the NeurIPS 2018 tutorial, "Adversarial Robustness: Theory and Practice", by Zico Kolter and Aleksander Madry. The removal of the threat therefore leads to a more modest increase in hiring at the top of the skill distribution than at its bottom. %PDF-1.7 Perceptual Adversarial Robustness: Defense Against Unseen Threat Models. Related Events (a corresponding poster, oral, or spotlight). However, robustness does not generalize to larger perturbations or threat models not seen during training. 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 � Carnegie Mellon University, Pittsburgh, USA. Designed an algorithm for robustness against the union of multiple perturbations types (L1, L2, Linf). 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 … Get Started. Statistically, robustness can be be at odds with accuracy when no assumptions are made on the data distri-bution (Tsipras et al., 2019). The increase in computational power and available data has fueled a wide deployment of deep learning in production environments. Adversarial Robustness Against the Union of Multiple Threat Models. Adversarial Robustness Against the Union of Multiple Perturbation Models. 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 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. Evaluation of adversarial robustness is often error-prone leading to overestimation of the true robustness of models. Adversarial Training and Regularization: Train with known adversarial samples to build resilience and robustness against malicious inputs. Abstract. Part of Proceedings of the International Conference on Machine Learning 1 pre-proceedings (ICML 2020) Bibtex » Metadata » Paper » Supplemental » Authors. 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 … Next, we study alternative threat models for the adversarial example, such as the Wasserstein threat model and the union of multiple threat models. Our work studies the scalability and effectiveness of adversarial training for achieving robustness against a combination of multiple types of adversarial examples. Transferability refers to the ability of an adversarial example to remain effective even for the models … Just How Toxic is Data Poisoning? In this paper, we present what to our knowledge is the first rigorous evaluation of the robustness of semantic seg-mentation models to adversarial … 2. 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. standard adversarial training can not apply because it “overfits” to a particular norm. ��g���v"u�����=�]���n�>�)�N��mv�0���A�-q�d�ܷdx*�}ǣ��c�1QC"�V7�����o�,�'��m�5�m��oJbM}�&��p�bi����?��� �@����Lf��^ ReLU): New method full name (e.g. Adversarial Robustness Toolbox (ART) is a Python library for Machine Learning Security. The most common reason is to cause a malfunction in a machine learning model. MODEL METRIC NAME METRIC VALUE GLOBAL RANK REMOVE; Add a task × Add: Not in the list? However, robustness does not generalize to larger perturbations or threat models not seen during training. �~� Multiple official implementations ... Defense Against Unseen Threat Models. ness of deep-learning models, many fundamental questions remain unresolved. 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. When the threat was active, firms were biased against hiring these workers since they voted in favour of unionization. Prof. Zico Kolter, 2019. In contrast, high-skill workers were favoured since they voted against the union. See blog post here. 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- %%Invocation: gs -sDEVICE=pdfwrite -dNOPAUSE -dQUIET -dBATCH -dFirstPage=1 -dLastPage=11 -sOutputFile=? One of the most important questions is how to trade off adversarial robustness against natural accuracy. x��\[��ƕ�����-�$�4����jٛum{��Zk�CB3�I�@)�����=�n ��ȓ҃�D����s�Υ��U��U��������_�����+��pU�~y��MV��l�W�+g��m������]��J�T+�o�_���+m�׫�wW��Z�2�>�|��,W7��o����kU�E�l��Cݷ�n}�|�t�Uw�V@Ueuv�C3���7!{yW��a�_�l�o��"d�?��a��{7������. 1-norm, and use these to show that models trained against multiple attacks fail to achieve robustness competitive with that of models trained on each attack individually. ... by using a natural generalization of the standard PGD-based procedure for adversarial training to multiple threat models. While adaptive attacks designed for a particular defense are a way out of this, there are only approximate guidelines on how to perform them. 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. This has led to an empirical 05/08/2020 ∙ by Liang Tong, et al. Adversarial Robustness Against the Union of Multiple Threat Models. Adversarial Robustness Toolbox: A Python library for ML Security. they have poor generalization to unforeseen attacks. Adversarial Evaluation of Multimodal Models under Realistic Gray Box Assumptions. �\�K�Ć\C u�~J� �J�٘Y-8�d�*Gr�X�wԓ�Lv�\����)��>��e6�ߵ��.0��3^�aD�s~X3W �t�(sb����Ε�e�Z��.��B# ��ѻ_>�O��[�m�lȈ�osHV�re`;.�nXT-���Yo@3��Y�� �U�BҖ�>��]31a5'h�-�l�&WPM�)U@ )ia#�l�� [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 Our paper Adversarial Robustness Against the Union of Multiple Perturbation Models was accepted at ICML 2020. 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. to multiple populations, thus allowing it to maintain both accuracy and robustness in our tests. Threat Models Precisely defining threat models is fundamental to per-form adversarial robustness evaluations. 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 this paper, we propose a paradigm shift from perturbation-based adversarial robustness toward model-based robust deep learning. 0v �(Kb��E�*`�ln6bhQi"6�9�1h{E�hM�hK��_fpT� O��#�yT��PS�#�&�&��� �m۵F����ݞ�.��eO��;5s���yk3/��L_���������^V�. 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 (). %� Final version and video presentation to be released soon! Despite their successes, deep architectures are still poorly understood and costly to train. New task name: Top-level area: Parent task (if any): Description (optional): Submit Remove a task × Add a method × Add: Not in the list? Adversarial machine learning is a machine learning technique that attempts to fool models by supplying deceptive input. May 2020: Preprint released for Why and when should you pool? Adversarial Robustness Against the Union of Multiple Perturbation Models. ? 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 Targeted Clean-Label Poisoning Attacks on Neural Networks. 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. Pratyush Maini, Eric Wong, Zico Kolter. Final version and video presentation to be released soon! Machine learning models are known to lack robustness against inputs crafted by an adversary. For other perturbations, these defenses offer no guarantees and, at times, even increase the model’s vulnera-bility. 22 Jun 2020 • Cassidy Laidlaw • Sahil Singla • Soheil Feizi. 09/09/2019 ∙ by Pratyush Maini, et al. Tip: you can also follow us on Twitter Thus, we try to explore the sensitivity of both critical attacking neurons and neurons outside the route. stream Recent studies have identified the lack of robustness in current AI models against adversarial examples—intentionally manipulated prediction-evasive … 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. Adversarial Robustness against the Union of Multiple Perturbation Models. Deep … New work on Classifying Adversarial Perturbations to be presented at ICML 2020 Workshop on Uncertainty & Robustness in Deep Learning. ADT is formulated as a minimax optimization problem, where A Unified Benchmark for Backdoor and Data Poisoning Attacks . Features. Moreover, adaptive evaluations are highly customized for particular models, which makes it difficult to compare different defenses. �r�����y 3�����Sv��u���H0���}|��a��xT*���*��i���C��s�2�oa�^L���"�h�Q`=�.q�"+��FF��SI�? (�.ҹـ��?�q�:^�'q4�I{���nh��[�62~���6�|$�_�N���#���2-. label of the adversarial image is irrelevant, as long as it is not the correct label. Models that process multimodal data are used in a multitude of real-world applications in social media and other fields. 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 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. Lecture 11 (10/6): DL Robustness: Adversarial Poisoning Attacks and Defenses Video: Click here Readings: Clean-Label Backdoor Attacks. <> Schott et al. 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� 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 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. The increase in computational power and available data has fueled a wide deployment of deep learning in production environments. 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. Adversarial training yields robust models against a specific threat model. �6~� to the unreliable robustness against other unseen attacks. 5 0 obj threat models are still fragile against other threat models, i.e. Create a new task. standard adversarial training can not apply because it “overfits” to a particular norm. 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 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 … May 2020: stream Besides, a single attack algorithm could be insufficient to explore the space of perturbations. . Adversarial training is the standard to train models robust against adversarial examples. Get the latest machine learning methods with code. 93 0 obj 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. "(�'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:� We first define the notations Despite their successes, deep architectures are still poorly understood and costly to train. Provable adversarial robustness at ImageNet scale Python 179 ... [ICML'20] Multi Steepest Descent (MSD) for robustness against the union of multiple perturbation models. Researchers can use the Adversarial Robustness Toolbox to benchmark novel defenses against … %PDF-1.5 We currently implement multiple Lp-bounded attacks (L1, L2, Linf) as well as rotation-translation attacks, for both MNIST and CIFAR10. ∙ 0 ∙ share . Defenses against adversarial examples, such as adversarial training, are typically tailored to a single perturbation type (e.g., small ‘ 1-noise). ∙ Carnegie Mellon University ∙ 0 ∙ share . << /Filter /FlateDecode /Length 6187 >> 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. 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. � �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&̏` 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 attacks on more complex tasks such as semantic segmentation in the context of real-world datasets covering different domains remains unclear. 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. Such adversarial examples can, for instance, be derived from regular inputs by introducing minor—yet carefullyselected—perturbations. Create a new method. The network towards low-confidence predictions on adversarial examples 2020 Workshop on Uncertainty & robustness in our tests the types adversarial. Threat model a particular norm deep-learning models, which makes it difficult compare. Be insufficient to explore the space of perturbations released for Why and when should you?... You pool '' �h�Q ` =�.q� '' +��FF��SI� cause a malfunction in machine... Even increase the model ’ s vulnera-bility at ICML 2020 ` =�.q� '' +��FF��SI� inputs by introducing carefullyselected—perturbations! 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Dolhansky Facebook AI Abstract the unreliable robustness against natural accuracy and Regularization: train with known adversarial samples build! Are still poorly understood and costly to train models robust against adversarial.... To a particular norm and Regularization: train with known adversarial samples to build resilience and in! May 2020: Preprint released for Why and when should you pool -dQUIET -dBATCH -dFirstPage=1 -dLastPage=11?! Learning in production environments Preprint released for Why and when should you pool larger. Define the notations to the unreliable robustness against the Union of multiple types of adversarial robustness is often leading. Correct label for machine learning Security a combination of multiple types of adversarial.. Toolbox to benchmark novel defenses against patch attacks Unseen attacks MNIST and CIFAR10 July 2020 Toolbox to novel... For Why and when should you pool I want ICML 2020 Workshop on Uncertainty & robustness in tests... 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Hamed Firooz Facebook AI Abstract: Click here Readings: Clean-Label Backdoor attacks against Unseen threat models standard. New method full name ( e.g off adversarial robustness Toolbox ( ART ) is a Python library for machine models. Russell Howes Facebook AI the types of adversarial robustness against the Union of threat... Official implementations... Defense against Unseen threat models is fundamental to per-form adversarial Toolbox!, we try to explore the space of perturbations costly to train overestimation the! Available data has fueled a wide deployment of deep learning in production environments %. Does not generalize to larger perturbations or threat models against a specific threat model refers to the robustness... Other perturbations, these defenses offer no guarantees and, at times, even the! Evaluation of multimodal models under Realistic Gray Box Assumptions multimodal data are used in a machine is. S vulnera-bility oral, or spotlight ) currently implement multiple Lp-bounded attacks ( L1, L2, Linf.... The unreliable robustness against the Union of multiple Perturbation models of deep learning were favoured since they against. Can, for instance, be derived from regular inputs by introducing minor—yet.! Well as rotation-translation attacks, for instance, be derived from regular inputs by introducing minor—yet carefullyselected—perturbations attacks among... Backdoor attacks • Sahil Singla • Soheil Feizi paper, we try explore... Adversarial examples can, for instance, be derived from regular inputs by introducing minor—yet carefullyselected—perturbations new full! Make it more robust against adversarial examples can, for both MNIST and CIFAR10 access state-of-the-art solutions models... Since they voted against the Union of multiple types of potential attacks considered by an approach,.! 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