arXiv:1905.02175. al. (2015) 1412.6572 Explaining and Harnessing Adversarial Examples. Outline. Left) Naively trained model. arXiv: 1904.12843. Right) Model with adversarial training. al. Generating adversarial examples • Fast gradient sign method: Find the gradient of the loss w.r.t. Adversarial examples p(x is panda) = 0.58 4 p(x is gibbon) = 0.99 [ICLR 15] Goodfellow, Shlens, and Szegedy. Authors: Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy (Submitted on 20 Dec 2014 , revised 25 Feb 2015 (this version, v2), latest version 20 Mar 2015 ) Abstract: Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying … [5] Kempka et al. Carlini et. Adversarial examples in the physical world. This idea was formulated by Ian et al. Szegedy, Christian, et al. ICLR 2015. ICLR (Poster) 2015. Source: Explaining and Harnessing Adversarial Examples, Goodfellow et al, ICLR 2015. Figure 3: Weight visualizations of maxout networks trained on MNIST. ICLR (Poster) 2014. (2019) Adversarial Examples Are Not Bugs, They Are Features. - “Explaining and Harnessing Adversarial Examples.” Goodfellow et al., ICLR 2014. arXiv. Contains materials for workshops pertaining to adversarial robustness in deep learning. && Shafahi et al. [4] Oh et al. Early attempts at explaining this phenomenon focused … for some slides. White-Box Adversary black-box adversary: Optimal perturbation , given loss : Original version of … ICML’18 workshop panda gibbon 2 : Towards evaluating the robustness of neural networks. What is an adversarial example? Explaining and Harnessing Adversarial Examples. - "Explaining and Harnessing Adversarial Examples" Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in … Why Do Adversarial Examples Exist? ViZDoom. ICLR(Poster) 2014. [] Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, Rob Fergus. Lec 21: Adversarial Robustness Yaoliang Yu July 21, 2020 1 Supervised Learning 2 Formally • Given a training set of pairs of examples Explaining and Harnessing Adversarial Examples (2015) Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy By now everyone’s seen the “panda” + “nematode” = “gibbon” photo (be l ow). “Explaining and harnessing adversarial examples.” ICLR 2015. (2019). +$ sgn ()(!,,∗) (! Adversarial attacks: this part will detail some famous adversarial attack methods with an aim to provide some insights of why adversarial examples exit and how to generate adversarial perturbation effectively and efficiently. Robust Audio Adversarial Example for a Physical Attack 1. Adversarial Examples aimed to mislead classification or detection at test time. This was one of the first and most popular attacks to fool a neural network. : Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples. correct class ! “Intriguing properties of neural networks.” Intl. ICLR’17: Kurakin et al, “Adversarial examples in the physical world”. Deep learning architectures are known to be vulnerable to adversarial examples, but previous work has focused on the application of adversarial examples to classification tasks. 7 Explaining and Harnessing Adversarial Examples, Ian J. Goodfellow and Jonathon Shlens and Christian Szegedy, 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings 2015 Adversarial examples can be defined as inputs or data that are perturbed in order to fool a machine learning network. arXiv 2016. Joint work with Tommaso Dreossi and Sanjit Seshia (Berkeley) 1. ICLR’14: Goodfellow et al, “Explaining and harnessing adversarial examples”. Deep generative models have recently become … Previous methods try to reduce the computational burden of adversarial training using single-step adversarial example generation schemes, which can effectively improve the efficiency but also introduce the problem of “catastrophic overfitting”, … International Conference on Learning Representations. 2015 Large Scale Business Discovery from Street Level Imagery. ICLR’15 Sound + noise = Audio Adversarial Examples: Targeted Attacks on Speech-to-Text. Abstract. You Only Propagate Once: Painless Adversarial Training Using Maximal Principle. High cost of training time caused by multi-step adversarial example generation is a major challenge in adversarial training. arXiv:1905.00877. ICML 2016. Google Proprietary … We explore methods of producing adversarial examples on deep generative mod-els such as the variational autoencoder (VAE) and the VAE-GAN. Explaining adversarial examples: Ilyas et al. ∗, take element-wise sign, update in resulting direction: # ← # + & sgn *+(#, ! in his paper “Explaining and Harnessing Adversarial Examples” from ICLR 2015 conference. Explaining and Harnessing Adversarial Examples Goodfellow, Ian J., Jonathon Shlens, and Chris@an Szegedy (ICLR 2015). 2015 Scalable, high-quality object detection. Title: Explaining and Harnessing Adversarial Examples. Computer Vision and Pattern Recognition. Explaining and harnessing adversarial examples. on Learning Representations (2014) [11] Nguyen, Anh, Jason Yosinski, and Jeff Clune. The following things are covered - Deep learning essentials; Introduction to adversarial perturbations Natural [8] Synthetic [1, 2] Simple Projected Gradient Descent-based attacks Goodfellow, Ian J., Jonathon Shlens, and Christian Szegedy. Overfitting to one metric •In “Explaining and Harnessing Adversarial Examples” I set up this game: •World samples an input point and label from the test set •Adversary perturbs point within the norm ball •Defender classifies the perturbed point •I expected this to be only moderately difficult and mostly solved quickly •> 2,000 papers later, still not really solved View lec21-rob.pdf from CS 480 at University of Waterloo. Early attempts at explaining this phenomenon focused … This tutorial creates an adversarial example using the Fast Gradient Signed Method (FGSM) attack as described in Explaining and Harnessing Adversarial Examples by Goodfellow et al. “Explaining and harnessing adversarial examples.” ICLR 2015; Michael Correl and Jeffrey Heer. Attack Gradient-base method. Moreover, adversarial examples are often transferable, i.e., adversaries crafted for one model can attack another model. CVPR’16: Moosavi-Dezfool et al, “DeepFool: A simple and accurate method to fool deep neural networks ”. : Explaining and Harnessing Adversarial Examples. While publications before this paper claimed that these adversarial examples were caused by nonlinearity and overfitting of … Box-constrained L-BFGS : Intriguing properties of neural networks. ICLR, 2015 (Link) [3] Carlini et al. I. Goodfellow, J. Shlens, and C. Szegedy. In the end of this part, we … Specifically, we will present five well-established works, including FGSM [1], C&W [2], DeepFool [3], JMSA [4], ZeroAttack [20]. (2019). (2014)cite arxiv:1412.6572. Google Proprietary Universal engineering machine (model-based optimization) Training data Extrapolation Make new inventions by finding input that maximizes model’s predicted performance. Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. arXiv. 2015 Going Deeper with Convolutions. ICML, 2018 (Link) Goodfellow et. Algorithmic Intelligence Lab •Adversarial examples raise issues critical to the “AI safety” in the real world •e.g. 2015 Explaining and Harnessing Adversarial Examples. Paper Alert@2015-11-30 Explaining Adversarial Examples 27. arXiv 2016. Adversarial Robustness Vision + = Explaining and Harnessing Adversarial Examples. Let’s look at an example. Most existing adversarial attack methods are iterative or optimization-based, consuming relatively long time in crafting adversarial examples. [10] Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus. 2015 Adversarial Robustness in Deep Learning. ∗) *# I. Goodfellow, J. Schlens, C. Szegedy, Explaining and harnessing adversarial examples, ICLR 2015 Adversarial Training for Free! • Iterative gradient sign method: take multiple small steps until misclassified, each time clip result to be within $-neighborhood TMM’20: Sanchez-Matilla et al, “Exploiting vulnerabilities of deep neural networks for privacy protection”. [] Thomas Tanay, Lewis D. Griffin. Conf. [2] Kurakin et al. Goodfellow, Ian J., Jonathon Shlens, and Christian Szegedy. ICLR. FGSM : Explaining and harnessing adversarial examples. - “Distributional Smoothing by Virtual Adversarial Examples.” Miyato et al ArXiv 2015. Plan • Part I [Adversarial ML] ~25mins • Different types of attacks • Test-time attacks • Defenses • Theoretical explorations • Part II [Opportunities in FM] … Adversarial examples are specialised inputs created with the purpose of confusing a … SP, 2017 (Link) [4] Athalye et al. Control of memory, active perception, and action in Minecraft. ← ! Explaining and Harnessing Adversarial Examples. Autonomous vehicles may misclassify graffiti stop signs Threat of Adversarial Examples *source: Eykholt et al., Robust Physical-World Attacks on Deep Learning Visual … : Explaining and Harnessing Adversarial Examples. Besides, the crafted examples usually underfit or overfit the source model, which reduces their … In 2017, another group demonstrated that it’s possible for these adversarial examples to generalize to the real world by showing that when printed out, an adversarially constructed image will continue to fool neural networks under different lighting and orientations: Intriguing properties of neural networks. Generating adversarial examples • Fast gradient sign method:! *source: Goodfellow et al., Explaining and Harnessing Adversarial Examples, ICLR 2015 9. Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Each row shows the filters for a single maxout unit. Request PDF | Generalizing Adversarial Examples by AdaBelief Optimizer | Recent research has proved that deep neural networks (DNNs) are vulnerable to adversarial examples… Faster adversarial training: Zhang et al. Source: Explaining and Harnessing Adversarial Examples, Goodfellow et al, ICLR 2015. ICLR, 2014 (Link) [2] Goodfellow et al. EXPLAINING AND HARNESSING ADVERSARIAL EXAMPLES + x Examples carefully crafted to-look like normal examples-cause misclassification x gibbon panda Ian J. Goodfellow et al. Adversarial Examples Somesh Jha Booz-Allen-Hamilton Colloqium (ECE@UMD) Thanks to Nicolas Papernot, Ian Goodfellow and Jerry Zhu. Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey. Goodfellow, I. J., Shlens, J., & Szegedy, C.: Explaining and harnessing adversarial examples. Explaining and Harnessing Adversarial Examples, ICLR Z15 Adversarial Examples [from lecture 1] Tape pieces make network predict a 45mph sign Robust Physical-World Attacks on Deep Learning Visual Classification, CVPR [18 Noisy attack: vision system thinks we now have a gibbon… Self-driving car: in each picture one of the 3 networks makes a mistake… DeepXplore: … ICLR 2014 [3] Goodfellow et al.
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