predictions is due to lower clean accuracy. YR��r~�?��d��F�h�M�ar:������I:�%y�� ��z�)M�)����_���b���]YH�bZ�@rH9i]L�z �����6@����X�p�+!�y4̲zZ� ��44,���ʊlZg|��}�81�x��կ�Ӫ��yVB��O�0��)���������bـ�i��_�N�n��[ �-,A+R����-I�����_'�l���g崞e�M>�9Q`!r�Ox�L��%۰VV�㢮��,�cx����bTI� �L5Y�-���kԋ���e���3��[ Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. Title:Adversarial Robustness May Be at Odds With Simplicity. Agreement NNX16AC86A, Is ADS down? Statistically, robustness can be be at odds with accuracy when no assumptions are made on the data distri-bution (Tsipras et al.,2019). Authors: Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Madry (Submitted on 30 May 2018 , last revised 9 Sep 2019 (this version, v5)) Abstract: 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 provably exists in a fairly simple and natural setting. How Does Batch Normalization Help Optimization?, [blogpost, video] Shibani Santurkar, Dimitris Tsipras, Andrew … the robustness of deep networks. Astrophysical Observatory. Robustness tests were originally introduced to avoid problems in interlaboratory studies and to identify the potentially responsible factors [2]. Robustness May Be at Odds with Accuracy Dimitris Tsipras*, Shibani Santurkar*, Logan Engstrom*, Alexander Turner, Aleksander Madry https://arxiv.org/abs/1805.12152 We show that adversarial robustness often inevitablely results in accuracy loss. Moreover, $\textit{there is a quantitative trade-off between robustness and standard accuracy among simple classifiers. predictions is always almost the same as robust accuracy, indicating that drops in robust accuracy is due to adversarial vulnerability. Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry: Exploring the Landscape of Spatial Robustness. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. RAIN: Robust and Accurate Classification Networks with Randomization and Enhancement. Tsipras et al. 1Tsipras et al, 2019: ’Robustness may be at odds with accuracy.’ 3 43 ETHZ Zürich, Switzerland Google Zürich. Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry. ... Tsipras D, Santurkar S, Engstrom L, Turner A, Madry A (2019) Robustness may be at odds with accuracy. is how to trade off adversarial robustness against natural accuracy. Tsipras D, Santurkar S, Engstrom L, Turner A, Madry A (2019) Robustness may be at odds with accuracy. Robustness may be at odds with accuracy. However, they are able to learn non-robust classifiers with very high accuracy, even in the presence of random perturbations. 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. 13/29 c Stanley Chan 2020. Computer Science - Computer Vision and Pattern Recognition; Computer Science - Neural and Evolutionary Computing. However, they are able to learn non-robust classifiers with very high accuracy, even in the presence of random perturbations. Schmidt L, Santurkar S, Tsipras D, Talwar K, ... Chen P, Gao Y (2018) Is robustness the cost of accuracy?—a comprehensive study on the robustness of 18 deep image classification models. 04/24/2020 ∙ by Jiawei Du, et al. %PDF-1.3 How Does Batch Normalization Help Optimization? (2019) showed that robustness may be at odds with accuracy, and a principled trade-off was studied by Zhang et al. A recent hypothesis [][] even states that both robust and accurate models are impossible, i.e., adversarial robustness and generalization are conflicting goals. Robustness May Be at Odds with Accuracy Dimitris Tsipras*, Shibani Santurkar*, Logan Engstrom*, Alexander Turner, Aleksander Madry ICLR 2019. Advances in Neural Information Processing Systems, 125-136, 2019. 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? The distortion is measure by ... Robustness may be at odds with accuracy, Tsipras et al., NeurIPS 2018. ICLR 2019. Robust Training of Graph Convolutional Networks via ... attains improved robustness and accuracy by respecting the latent manifold of ... Tsipras et al. %��������� ∙ 0 ∙ share . 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. A Ilyas, S Santurkar, D Tsipras, L Engstrom, B Tran, A Madry. is how to trade off adversarial robustness against natural accuracy. Adversarial Robustness through Local Linearization, ... Robustness may be at odds with accuracy, Tsipras et al., NeurIPS 2018. Robustness May Be at Odds with Accuracy. (2019), which de- This bound implies that if p < 1, as standard accuracy approaches 100% (d!0), adversarial accuracy falls to 0%. Robustness May Be at Odds with Accuracy | Papers With Code Robustness May Be at Odds with Accuracy ICLR 2019 • Dimitris Tsipras • Shibani Santurkar • Logan Engstrom • Alexander Turner • Aleksander Madry We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. Figure 2 qualitatively compares SmoothGrad and simple gradients. ]��u|| /]��,��D�.�i>OP�-�{0��Û��ۃ�S���j{������.,gX�W�C�T�oL�����٬���"+0~�>>�N�Fj��ae��}����&. Gradient Regularization Improves Accuracy of Discriminate Models Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate Convergence of Gradient Descent on Separable Data The Implicit Bias of Gradient Descent on Separable Data CINIC-10 Is Not ImageNet or CIFAR-10 BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop Theory … ��& ��RTBҪD_W]2��)>�x�O����hx���/�{gnݟVw��N3? 这篇说adbersarial training会伤害classification accuracy. Tsipras et al. Furthermore, recent works Tsipras et al. 1Tsipras et al, 2019: ’Robustness may be at odds with accuracy. Robustness May Be at Odds with Accuracy. Proceedings of the International Conference on Representation Learning (ICLR …, 2018. arXiv preprint arXiv:1805.12152, 1, 2018. This may focus the salience map on robust features only, as SmoothGrad highlights the important features in common over a small neighborhood. Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Madry: Robustness May Be at Odds with Accuracy. 2 Tehran Polytechnic Iran. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry. Deep networks were recently suggested to face the odds between accuracy (on clean natural images) and robustness (on adversarially perturbed images) (Tsipras et al., 2019). 4 0 obj We show that neither robustness nor non-robustness are monotonic with changing the number of bits for the representation and, also, neither are preserved by quantization from a real-numbered network. 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. D Tsipras, S Santurkar, L Engstrom, A Turner, A Madry. (2019); Ilyas et al. Mark. Models trained on highly saturated CIFAR10 are quite robust and the gap between robust accuracy and robustness w.r.t. We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. ’ 3. Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors. arXiv preprint arXiv:1805.12152, 2018. An Unexplained Phenomenon Models trained to be more robust to adversarial attacks seem to exhibit ’interpretable’ saliency maps1 Original Image Saliency map of a robusti ed ResNet50 This phenomenon has a remarkably simple explanation! Shibani Santurkar [0] Logan Engstrom [0] Alexander Turner. Authors:Preetum Nakkiran. This has led to an empirical line of work on adversarial defense that incorporates var-ious kinds of assumptions (Su et al.,2018;Kurakin et al., 2017). Full Text. ICLR (Poster) 2019. In: International conference on learning representations. accuracy. We show that Parseval networks match the state-of-the-art in terms of accuracy on CIFAR-10/100 and Street View House Numbers (SVHN) while being more robust … A Ilyas, S Santurkar, D Tsipras, L Engstrom, B Tran, A Madry. Statistically, robustness can be be at odds with accuracy when no assumptions are made on the data distri-bution (Tsipras et al., 2019). These findings also corroborate a similar phenomenon observed empirically in more complex settings. 1Tsipras et al, 2019: ’Robustness may be at odds with accuracy.’ 3. ICLR 2019. Aleksander Madry [0] international conference on learning representations, 2019. ICLR 2019. … Robustness May Be at Odds with Accuracy, Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Mądry. arXiv preprint arXiv:1805.12152, 2018. With adversarial input, adversarial training yields the best performance as we expect. This has led to an empirical line of work on adversarial defense that incorporates var- ious kinds of assumptions (Su et al., 2018; Kurakin et al., 2017). D Tsipras; S Santurkar; L Engstrom; A Turner ; A Madry; Adversarial training for free! .. Logan Engstrom*, Brandon Tran*, Dimitris Tsipras*, Ludwig Schmidt, and Aleksander Mądry. (2019) claim that existence of adversarial examples are due to standard training methods that rely on highly predictive but non-robust features, and make connections between robustness and explainability. Robustness May Be at Odds with Accuracy Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Madry (Submitted on 30 May 2018 (v1), last revised 11 Oct 2018 (this version, v3)) We show that there exists an inherent tension between the goal of adversarial robustness and that of standard generalization. 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. Title:Adversarial Robustness May Be at Odds With Simplicity. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. found ... With unperturbed data, standard training achieves the highest accuracy and all defense techniques slightly degrade the performance. There is another very interesting paper Tsipras et al., Robustness May Be at Odds with Accuracy, arXiv: 1805.12152 Some observations are quite intriguing. Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers. Robustness May Be at Odds with Accuracy. stream Code for "Robustness May Be at Odds with Accuracy" Jupyter Notebook 13 81 2 1 Updated Nov 13, 2020. mnist_challenge A challenge to explore adversarial robustness of neural networks on MNIST. moosavi.sm@gmail.com smoosavi.me. This means that a robustness test was performed at a late stage in the method validation since interlaboratory studies are performed in the final stage. .. Models trained to be more robust to adversarial attacks seem to exhibit ’interpretable’ saliency maps1 Original Image Saliency map of a robusti ed ResNet50 This phenomenon has a remarkably simple explanation! Use, Smithsonian Statistically, robustness can be be at odds with accuracy when no assumptions are made on the data distri- bution (Tsipras et al., 2019). Robustness May Be at Odds with Accuracy, Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Mądry. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. Published as a conference paper at ICLR 2019 ROBUSTNESS MAY BE AT ODDS WITH ACCURACY Dimitris Tsipras∗ , Shibani Santurkar∗ , Logan Engstrom∗ , Alexander Turner, Aleksander M ˛ adry Massachusetts Institute of Technology {tsipras,shibani,engstrom,turneram,madry}@mit.edu ABSTRACT We show that there exists an inherent tension between the goal of adversarial robustness and that of … 3 EPFL Lausanne, ... last column measures the minimum average pixel level distortion necessary to reach 0% accuracy on the training set. For this reason, we introduce a verification method for quantized neural networks which, using SMT solving over bit-vectors, accounts for their exact, bit-precise semantics. Robustness may be at odds with accuracy. (or is it just me...), Smithsonian Privacy 43 ETHZ Zürich, Switzerland Google Zürich. ICLR 2019. EI. Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Madry We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. Theorem 2.1(Robustness-accuracy trade-off). Notice, Smithsonian Terms of We built a … Cited by: 20 | Bibtex | Views 27 | Links. 44 Interested in my research? Authors:Preetum Nakkiran. Title: Robustness May Be at Odds with Accuracy. Andrew Ilyas*, Logan Engstrom*, Ludwig Schmidt, and Aleksander Mądry. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative Abstract: Current techniques in machine learning are so far are unable to learn classifiers that are robust to adversarial perturbations. Along with the extensive applications of CNN models for classification, there has been a growing requirement for their robustness against adversarial examples. Dimitris Tsipras. Robustness may be at odds with accuracy. Robustness may be at odds with accuracy. Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors , Andrew Ilyas, Logan Engstrom, Aleksander Mądry. In: International conference on learning representations. Advances in Neural Information Processing Systems, 125-136, 2019. Any classifier that attains at least 1dstandard accuracy on D has robust accuracy at mostp 1 pdagainst an ‘¥-bounded adversary with#2h. 1. ICLR 2019 • Dimitris Tsipras • Shibani Santurkar • Logan Engstrom • Alexander Turner • ... 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