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Using 3 popular image classification tasks, MNIST, CIFAR10, and ImageNet, the authors show that their attacks can generate an AE for any chosen target class, with a 100% success rate. An effective defense will likely need to be adaptive, capable of learning as it gathers information from attempted attacks. Neural networks provide state-of-the-art results for most machine learning tasks. Robust optimization includes methods for making deep neural networks behave more robustly to the presence of adversarial perturbations in the input, which is the primary focus of our taxonomy in Section 2. Towards Evaluating the Robustness of Neural Networks Abstract: Neural networks provide state-of-the-art results for most machine learning tasks. The existing distilled network fails as the optimization gradients are almost always zero, resulting in both L-BFGS and FGSM (Fast Gradient Sign Method) failing to make progress and terminate. 11/15/2020 ∙ by Yuxin Wen, et al. Summary contributed by Shannon Egan, Research Fellow at Building 21 and pursuing a master’s in physics at UBC. However the problem of NN susceptibility to AEs will not be solved by these attacks. Among the more established techniques to solve the problem, one is to require the model to be ϵ-adversarially robust (AR); that is, to … In this paper, Carlini and Wagner devise 3 new attacks which show no significant performance decrease when attacking a defensively “distilled” NN. Title: Towards Evaluating the Robustness of Neural Networks. However, this is inefficient, as the set of inputs is large. Crucially, the new attacks are effective against NNs trained by defensive distillation, which was proposed as a general-purpose defense against AEs. Defensive distillation is robust for current level of attacks, it fails against stronger attacks. In this paper Carlini and Wagner highlight an important problem: there is no consensus on how to evaluate whether a network is robust enough for use in security-sensitive areas, such as malware detection and self-driving cars. Defining humanity's place in a world of algorithms. Furthermore, the adversarial images are often visually indistinguishable from the originals. Crucially, the new attacks are effective against NNs trained by defensive distillation, an alternative supervised learning approach which was invented to prevent overfitting. AEs are manipulated images x’ which remain extremely close, as measured by a chosen distance metric, to an input x with correct classification C*(x), and yet are misclassified as C(x’) =/= C*(x). Towards Evaluating the Robustness of Neural Networks. In future, a defense which is effective against these methods may be proposed, only to be defeated by an even more powerful (or simply different) attack. 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