explaining and harnessing adversarial examples iclr

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