Towards Evaluating the Robustness of Neural Networks. In it, we develop three attacks against neural networks to produce adversarial examples (given an instance x, can we produce an instance x' that is visually similar to x but is a different class). Neural networks provide state-of-the-art results for most machine learning tasks. README.md About. Slides. The attacks proposed by Carlini and Wagner are a step towards better robustness testing, but NN vulnerability to AEs remains an important open problem. 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. arXiv preprint arXiv:1610.01239 (2017) However their vulnerability to attacks, including adversarial examples (AEs), are a major barrier to their use in security-critical decisions. since training and evaluating such networks is costly in terms of runtime and memory, this method is impractical for neural networks. Abstract We consider how to measure the robustness of a neural network against adversarial examples. These approach helps in establishing robustness and developing high-confidence adversarial examples. Towards Evaluating the Robustness of Neural Networks Abstract: Neural networks provide state-of-the-art results for most machine learning tasks. Make ML robust Make ML better. The method of bagging cannot be directly applied to large neural networks as it involves training multiple models, and evaluating multiple models on each test example. Summary by David Stutz 2 years ago Carlini and Wagner propose three novel methods/attacks for adversarial examples and show that defensive distillation is not effective. One can even choose an arbitrary target class t, and optimize the AE such that C(x’) = t.  The stereotypical AE in image classification is so close to its base image that a human would not be able to distinguish the original from the adversarial by eye. Video: Click here Readings: Towards Deep Learning Models Resistant to Adversarial Attacks. However, this is inefficient, as the set of inputs is large. Corresponding code to the paper "Towards Evaluating the Robustness of Neural Networks" by Nicholas Carlini and... Running attacks. *Author & link to original paper at the bottom. Results in Brief The robustness of neural networks to adversarial examples has received great attention due to security implications. Towards Evaluating the Robustness of Neural Networks; Tuesday March 31: Homework 3 due. c does not have to increase significantly to produce an AE with the desired target classification). Why should we care about adversarial examples? These results suggest that stronger defenses are needed to ensure robustness against AEs, and NNs should be vetted against stronger attacks before being deployed in security-critical areas. A team of scientist from the University of California, Berkeley develop three attack algorithms to evaluate the robustness of the image classification neural networks. Defensive distillation is a defense proposed for hardening neural networks against adversarial examples whereby it defeats existing attack algorithms and reduces their success probability from 95% to 0.5%. This is achieved by training the network twice: the first time using the standard approach of inputting only the correct label to the cost function; and the second time using the “soft labels” which indicate the probability of each class, returned by the network itself after the initial training. Title:Towards Evaluating the Robustness of Neural Networks. The L2 attack supports a batch_size paramater to run attacks in parallel. A larger c indicates that a larger manipulation is required to produce the target classification. Ethics and compliance professional with experience in fraud investigation, forensic accounting, anti-corruption reviews, ethics advisory and litigation support experience. A Case Study on Neural Image Captioning Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, Luca Daniel The robustness of neural networks to adversarial examples has received great attention due to security implications. In this dissertation, we introduce a general framework for evaluating the robustness of neural network through optimization-based methods. Summary contributed by Sundar Narayanan, Director at Nexdigm. A Note on Lazy Training in Supervised Differentiable Programming. If you have any … Among the more established techniques to solve the problem, one is to require the model to be ϵ-adversarially robust (AR); that is, to … All rights reserved 2020. While the L0 distance metric is non-differentiable, L2 appears to be effective. Unfortunately, neural networks are vulnerable to adversarial examples: given an input x and any target classification t, it is possible to find a new input x ′ that is similar to x but classified as t. .. The former approach, while sound, is substantially more difficult to implement in practice, and all attempts have required approximations,. The distilled network works in 4 steps, namely (1) Teach the teacher network with standard set, (2) Create a Soft label on the training set using the teacher network, (3) Train the distilled network on soft labels and (4) Test the distilled network. The L2 and  L∞ attacks are especially effective, only requiring a small c to achieve the desired classification (and therefore a small manipulation of the input). robustness analysis: evaluating the intrinsic model robustness to adversarial perturbations to normal examples. The latter term is multiplied by a constant c, which is used as a proxy for the aggressiveness of the attack. Neural networks provide state-of-the-art results for most machine learning tasks. The powerful attacks proposed by Carlini and Wagner are a step towards better robustness testing, but NN vulnerability to AEs remains an open problem. All rights reserved 2020. However their vulnerability to attacks, including adversarial examples (AEs), is a major barrier to their application in security-critical decisions. Unfortunately, neural networks are vulnerable to adversarial examples: given an input x and any target classification t, it is possible to find a new input x' that is similar to x but classified as t. Adversary Resistant Deep Neural Networks with an Application to Malware Detection. Summary contributed by Shannon Egan, Research Fellow at Building 21 and pursuing a master’s in physics at UBC. 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. While defensive distillation blocks AEs generated by L-BFGS, fast gradient sign, DeepFool and JSMA, the new attacks still achieve a 100% success rate at finding an AEs, with minimal increase in the aggressiveness of the attack. 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. Crucially, the new attacks are effective against NNs trained by defensive distillation, an alternative supervised learning approach which was invented to prevent overfitting. In general, there are two different approaches one can take to evaluate the robustness of a neural network: attempt to prove a lower bound, or construct attacks that demonstrate an upper bound. Evaluating the robustness of a network on multiple samples in a dataset, with good support for pausing and resuming evaluation or running optimizers with different parameters; MNIST and CIFAR10 datasets for verification; Sample neural networks, including the networks verified in our paper. © MONTREAL AI ETHICS INSTITUTE. While defensive distillation blocks AEs generated by L-BFGS, fast gradient sign, DeepFool and JSMA, the new attacks still achieve a 100% success rate at finding an AE, with minimal increase in the aggressiveness of the attack (i.e. Robust … In this paper, Carlini and Wagner devise 3 new attacks which show no significant performance decrease when attacking a defensively “distilled” NN. The paper is set on the broad premise of robustness of neural network to avert an adversarial attack. *Author & link to original paper at the bottom. 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. AEs are manipulated inputs x’ which are extremely similar to an input x with correct classification C*(x), and yet are misclassified as C(x’) =/= C*(x). The L2 and L∞ attacks are especially effective, only requiring a small c to achieve the desired classification. Towards Evaluating the Robustness of Neural Networks ... DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples. Towards Evaluating the Robustness of Neural Networks At ICLR’18, we introduced a robustness metric called CLEVER (Cross Lipschitz Extreme Value for nEtwork Robustness) and its extension (CLEVER++) to help you evaluate how robust your trained neural network is to resist the Lp-norm based adversarial attacks. It lays out the two clear factors (a) Construct proofs of lower bound for robustness and (b) Demonstrate attacks for upper bound on robustness. Defensive distillation is robust for current level of attacks, it fails against stronger attacks. When compared to existing algorithms for generating AEs, including Szegedy et al.’s L-BFGS, Goodfellow et al.’s fast gradient sign method (FGS), Papernot et al.’s Jacobian-based Saliency Map Attack (JSMA), and Deep-fool, Carlini and Wagner’s AEs fool the NNs more often, with less severe modification of the initial input. Neural networks provide state-of-the-art results for most machine learning tasks. However the problem of NN susceptibility to AEs will not be solved by these attacks. We should also look to general properties of AE behaviour for guidance. The paper is set on the broad premise of robustness of neural network to avert an adversarial attack. Authors: ... > In this paper, we demonstrate that defensive distillation does not significantly increase the robustness of neural networks by introducing three new attack algorithms that are successful on both distilled and undistilled neural networks with $100%$ probability. The stronger attacks proposed by Carlini and Wagner are important for demonstrating the vulnerabilities of defensive distillation, and for establishing a potential baseline for NN robustness testing. are often effective against a completely different network; even eliciting the same faulty classification. 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. Slides. This prevents oscillation resulting in effective results. Full summary: Neural networks (NNs) have achieved state-of-the-art performance on a wide range of machine learning tasks, and are being widely deployed as a result. 16 Aug 2016 • Nicholas Carlini • David Wagner. This also eliminates some pixels that don’t have much effect on the classifier output. Unfortunately, neural networks are vulnerable to adversarial examples: given an input and any target classification , it is possible to find a new input that is similar to but classified as . 11/15/2020 ∙ by Yuxin Wen, et al. The authors’ new attacks generate an AE by minimizing the sum of two terms: 1) The  L2, L0, or L∞ distance between the original input and the presumptive adversarial and 2) an objective function that penalizes any classification other than the target. The authors apply the distance metrics using three solvers gradient descent, gradient descent with momentum and ADAM. To address this, they develop 3 adversarial attacks which prove more powerful than existing methods. The former approach, while sound, is substantially more difficult to implement in practice, and all attempts have required approximations [ 2 , 21 ] . The problem of adversarial examples has shown that modern Neural Network (NN) models could be rather fragile. The powerful attacks proposed by Carlini and Wagner are a step towards better robustness testing, but NN vulnerability to AEs remains an open problem. The following are the key takeaways the paper explores as a defense to the adversarial attack and as a step forward from distillated network approach, Original paper by Nicholas Carlini, David Wagner: https://arxiv.org/abs/1608.04644, Creative Commons Attribution 4.0 International License, Defenders should make sure to establish robustness against the L2 distance metric, Demonstrate that transferability fails by constructing high-confidence adversarial examples. Title: Towards Evaluating the Robustness of Neural Networks. In ICLR (Workshop Track) (2017) Wang, Q. et al. This could be any type of misclassification (General misclassification, Targeted misclassification or source/ target misclassification). Towards Understanding the Regularization of Adversarial Robustness on Neural Networks. Towards Evaluating the Robustness of Neural Networks Neural networks provide state-of-the-art results for most machine learning tasks. Paper Discussion: Generative Adversarial Nets. One key to better defenses may be the transferability principle, a phenomenon whereby AEs generated for a certain choice of architecture, loss function, training set etc. The third category in this review paper is to detect the presence of adversarial examples in the input in order to protect trained classifiers. High-confidence adversarial examples are the ones where an adversarial example gets strongly misclassified by the original model, instead of barely changing the classification. On the other hand, the authors attempt 3 types of attacks based on the distance metrics namely L0, L2 and L∞. Robust Neural Network Attacks The following code corresponds to the paper Towards Evaluating the Robustness of Neural Networks . All 3 attacks generate an AE by minimizing the sum of two terms: 1) The L2, L0  or L∞ distance between the original input and the presumptive AE and 2) an objective function which penalizes any classification other than a chosen target class. Crucially, the new attacks are effective against NNs trained by defensive distillation, which was proposed as a general-purpose defense against AEs. Defining humanity's place in a world of algorithms. Defensive distillation is a defense proposed for hardening neural networks against adversarial examples whereby it defeats existing attack algorithms and reduces their success probability from 95% to 0.5%. They find the results to be effective in the distilled network environment. In general, there are two different approaches one can take to evaluate the robustness of a neural network: attempt to prove a lower bound, or construct attacks that demonstrate an upper bound. Recently, adversarial deception becomes one of the most considerable threats to deep neural networks. An effective defense will likely need to be adaptive, capable of learning as it gathers information from attempted attacks. Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations Jeet Mohapatra1, Tsui-Wei Weng1, Pin-Yu Chen2, Sijia Liu2and Luca Daniel1 1MIT EECS,2MIT-IBM Watson AI Lab, IBM Research Abstract Verifying robustness of neural networks given a specified threat model is a fundamental yet challenging task. Unfortunately, neural networks are vulnerable to adversarial … 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. Original paper by Nicholas Carlini and David Wagner: https://arxiv.org/abs/1608.04644, Creative Commons Attribution 4.0 International License. As we are working with a set of inputs, a straightforward approach is to perform robustness evaluation for the inputs individually and to then merge the results. Adversarial training is a defense technique that improves adversarial robustness of a deep neural network (DNN) by including adversarial examples in the training data. One promising defense mechanism, known as defensive distillation, has been shown to reduce the success rate of existing AE generation algorithms from 95% to 0.5%. However, compared to extensive research in new designs of various adversarial attacks and defenses, the neural networks' intrinsic robustness property is still lack of thorough investigation. © MONTREAL AI ETHICS INSTITUTE. Towards Deep Learning Models Resistant to Adversarial Attacks; Thursday April 9: Homework 3 makeup due. This tutorial will particularly highlight state-of-the-art techniques inadversarial attacks and robustness verification of deep neural networks (DNNs).We will also introduce some effective countermeasures to improve robustness ofdeep learning models, with a particular focus on generalisable adversarial train-ing. However, due to the sampling-based approach of estimati… Furthermore, the adversarial images are often visually indistinguishable from the originals. Towards Evaluating the Robustness of Neural Networks. We look carefully at a paper from Nicholas Carlini and David Wagner ("Towards Evaluating the Robustness of Neural Networks", 2017). Towards Evaluating the Robustness of Neural Networks Nicholas Carlini David Wagner Google UC Berkeley. Neural networks (NNs) have achieved state-of-the-art performance on a wide range of machine learning tasks. This makes it difficult to apply neural networks in security-critical areas. L2 attempts to identify unimportant pixels in the image in each iteration resulting in inherently bringing focus to important pixels, perturbation of which will impact the classification. Furthermore, the adversarial images are often indistinguishable from the originals. Despite the fact that AEs exist, and moreover have proven easy to generate, there is little consensus on how to test NNs for robustness against adversarial attacks, and even less on what constitutes an effective defense. Unfortunately, neural networks are vulnerable to adversarial examples: given an input x and any target classification t, it is possible to find a new input x' that is similar to x but classified as t. This makes it difficult to apply neural networks in security-critical areas. Lecture 8 (9/24): DL robustness: Adversarial attacks and defenses. 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). Defensive distillation’s inefficacy against these more powerful attacks underlines the need for better defenses against AEs. We apply our framework to two different domains, image recognition and automatic speech recognition, and find it provides state-of-the-art results for both. Defensive distillation is a recently proposed approach that can take an arbitrary neural network, and increase its robustness, reducing the success rate of current attacks' ability to find adversarial examples from $95\%$ to $0.5\%$. ∙ 0 ∙ share . The paper attempts to move towards the second while explaining the gaps in first (essentially the weakness of distilled networks). If c is too small, the resulting AE may fail to fool the network. To exploit the parallelism offered by GPUs, our approach uses tensors. Although in principle the means of tackling these two problems are expected to be inde-pendent, that is, the evaluation of a neural network’s intrinsic robustness should be agnostic to attack CLEVER has theoretical grounding based on Lipschitz continuity of the classifier model f and is scalable to state-of-the-art ImageNet neural network classifiers such as GoogleNet, ResNet and many others. Despite various attack approaches to crafting visually imperceptible adversarial examples, little has been developed towards a comprehensive measure of robustness. A strong defense against AEs will have to somehow break transferability, otherwise an attacker could generate AEs on a network with weaker defenses, and simply transfer them to the more robust network. The latter term is multiplied by a constant c, with larger c corresponding to a more “aggressive” attack and larger manipulation of the input. Neural networks (NNs) have achieved state-of-the-art performance on a wide range of machine learning tasks, and are being widely deployed as a result. The paper also reflects that high confidence adversarial attack limits/ breaks the transferability of the adversarial attack to different models. L∞ replace the L2 term in the objective function with a penalty for any terms that exceed τ (initially 1, decreasing in each iteration). Defining humanity's place in a world of algorithms. Neural Tangent Kernel: Convergence and Generalization in Neural Networks. Each attack has many …

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