The adversarial example xâ is then generated by scaling the sign information by a parameter ε (set to 0.07 in the example) and adding it to the original image x. 13 0 obj EXPLAINING AND HARNESSING ADVERSARIAL EXAMPLES + x Examples carefully crafted to-look like normal examples-cause misclassification x gibbon panda What is an adversarial example? We construct targeted audio adversarial examples on automatic speech recognition. Given any audio waveform, we can produce another that is over 99.9% similar, but transcribes as any phrase we choose (recognizing up to 50 characters per second of audio). Explaining and harnessing adversarial examples Goodfellow, Ian J., Jonathon Shlens, and Christian Szegedy. (2014)cite arxiv:1412.6572. Summary Szegedy et al [1] made an intriguing discovery: several machine learning models, including state-of-the-art neural networks, are vulnerable to adversarial examples. x���P(�� �� >> Explaining and harnessing adversarial examples. - âExplaining and Harnessing Adversarial Examples.â Goodfellow et al., ICLR 2014. Predicting Adversarial Examples with High Confidence, Hitting Depth : Investigating Robustness to Adversarial Examples in Deep Convolutional Neural Networks, Intriguing Properties of Adversarial Examples, Deep neural rejection against adversarial examples, Vulnerability of classifiers to evolutionary generated adversarial examples, Adversarial Examples on Object Recognition: A Comprehensive Survey, Harnessing Model Uncertainty for Detecting Adversarial Examples, Learning Universal Adversarial Perturbations with Generative Models, Towards Deep Neural Network Architectures Robust to Adversarial Examples, Dropout: a simple way to prevent neural networks from overfitting, Deep neural networks are easily fooled: High confidence predictions for unrecognizable images, Learning Multiple Layers of Features from Tiny Images, Blog posts, news articles and tweet counts and IDs sourced by, View 8 excerpts, cites methods and background, 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), View 3 excerpts, cites background and methods, View 9 excerpts, cites methods and background, View 5 excerpts, cites methods and background, 2018 IEEE Security and Privacy Workshops (SPW), View 3 excerpts, references background and results, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), View 5 excerpts, references background and methods, View 4 excerpts, references background and methods, View 2 excerpts, references methods and background, By clicking accept or continuing to use the site, you agree to the terms outlined in our, Explaining and Harnessing Adversarial Examples, Gradient-based Adversarial Attacks : An Introduction, Creating Adversarial Examples with JAX from the scratch, Perturbation Theory in Deep Neural Network (DNN) Training, Five Components Of Autonomous Car Security, 科学人热点 这也许将是围棋AI的最后死穴 不久的将来，围棋AI就将彻底碾压人类。那时我们是不是只能甘居二流棋手了呢？未必，因为AI还有至少一个盲区：“对抗样本”。 Ent, Google launches TensorFlow machine learning framework for graphical data. They propose Fast Gradient Sign Method (FGSM) harness adversarial samples. << endobj 20 0 obj I. Goodfellow, J. Shlens, and C. Szegedy. /Type /XObject EXPLAINING AND HARNESSING ADVERSARIAL EXAMPLES (Goodfellow et al. Adversarial examples are specialised inputs created with ⦠<< I. Goodfellow, J. Shlens, and C. Szegedy. Source: Explaining and Harnessing Adversarial Examples, Goodfellow et al, ICLR 2015. [Tensorflow.js] AdVis: Exploring real-time Adversarial Attacks in the browser with Fast Gradient Sign Method. Early attempts at explaining this ⦠Early attempts at explaining this phenomenon focused on nonlinearity and overfitting. /Length 15 The library may be used to develop more robust machine learning models and to provide standardized benchmarks of modelsâ performance in the adversarial setting. â, take element-wise sign, update in resulting direction: # â # + & sgn *+(#, ! Presented by Jonathan Dingess << /Subtype /Form AUTHORS: Espoir K. Kabanga, Chang Hoon Kim What is Adversarial Examples Setup: A trained CNN to classify images An adversarial example is an instance with small, intentional perturbations that cause a machine learning model to make a false prediction. Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. What is an adversarial example? /ProcSet [ /PDF ] Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. (Goodfellow 2016) Adversarial Examples Timeline: âAdversarial Classiï¬cationâ Dalvi et al 2004: fool spam ï¬lter âEvasion Attacks Against Machine Learning at Test Timeâ Biggio 2013: fool neural nets Szegedy et al 2013: fool ImageNet classiï¬ers imperceptibly Goodfellow et al 2014: cheap, closed form attack Explaining and Harnessing Adversarial Examples (2015) Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy By now everyoneâs seen the âpandaâ + ânematodeâ = âgibbonâ photo (be l ow). presentation by Ian Goodfellow. endstream >> << arXiv preprint arXiv:1412.6572. >> Source: Explaining and Harnessing Adversarial Examples, Goodfellow et al, ICLR 2015. /Matrix [1 0 0 1 0 0] /Resources 20 0 R /BBox [0 0 8 8] x���P(�� �� 21 0 obj /Filter /FlateDecode 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.This was one of the first and most popular attacks to fool a neural network. /Subtype /Form - âIntriguing Properties of Neural Networks.â Szegedy et al., ICLR 2014. /FormType 1 /Type /XObject This method works by taking the sign of the gradient of the loss function with respect to the input. Foresight (2010). 2015. Itâs easy to attain high confidence in the incorrect classification of an adversarial example. Early attempts at explaining this ⦠Abstract. << /S /GoTo /D [15 0 R /Fit] >> 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 preprint arXiv:1312.6199 (2013). 2015) This paper proposes a novel way of creating adversarial examples very fast. /Type /XObject endobj x��XYo7~�_��]���>����p�x�h�}0�'�v��/)
5��؍�IZ�jD��Q�8#�%l>k��Q�� ��S. >> cleverhans is a software library that provides standardized reference implementations of adversarial example construction techniques and adversarial training. Early attempts at explaining this phenomenon focused ⦠They use the ⦠Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Explaining and Harnessing Adversarial Examples. I recommend reading the chapter about Counterfactual Explanations first, as the concepts are very similar. endobj /FormType 1 /FormType 1 endobj /ProcSet [ /PDF ] << 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. %���� An adversarial example is an instance with small, intentional feature perturbations that cause a machine learning model to make a false prediction. Figure: From Explaining and Harnessing Adversarial Examples by Goodfellow et al. Letâs look at an example. Second, the adversarial examples donât depend much on the specific deep neural network used for the task â an adversarial example trained for one network seems to confuse ⦠/Resources 18 0 R Adversarial examples are specialised inputs created with the purpose ⦠Goodfellow, I., Shlens, J. and Szegedy, C. (2015) Explaining and Harnessing Adversarial Examples. 2015 International Conference on Learning Representations. /ProcSet [ /PDF ] These intentionally-manipulated inputs attempt to mislead the targeted model while maintaining the appearance of innocuous input data. >> stream ConvNets express a differentiable function from the pixel values to class scores endobj arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. â) *# I. Goodfellow, J. Schlens, C. Szegedy, Explaining and harnessing adversarial examples⦠In ICLR. They generated adversarial examples on a deep maxout network and classified these examples using a shallow softmax network and a shallow RBF network. However, researchers found that deep neural networks, as the core algorithm of deep learning technology, are vulnerable to adversarial examples. The adversarial examples are some special input examples which were added small magnitude and carefully crafted perturbations ⦠endobj We apply our white-box iterative optimization-based attack to Mozilla's implementation ⦠/Shading << /Sh << /ShadingType 3 /ColorSpace /DeviceRGB /Domain [0.0 8.00009] /Coords [8.00009 8.00009 0.0 8.00009 8.00009 8.00009] /Function << /FunctionType 3 /Domain [0.0 8.00009] /Functions [ << /FunctionType 2 /Domain [0.0 8.00009] /C0 [0.5 0.5 0.5] /C1 [0.5 0.5 0.5] /N 1 >> << /FunctionType 2 /Domain [0.0 8.00009] /C0 [0.5 0.5 0.5] /C1 [1 1 1] /N 1 >> ] /Bounds [ 4.00005] /Encode [0 1 0 1] >> /Extend [true false] >> >> In simpler words, these various models misclassify images when subjected to small changes. << /Filter /FlateDecode arXiv preprint arXiv:1412.6572 (2014). arXiv is committed to these values and only works with partners that adhere to them. CONFERENCE PROCEEDINGS Papers Presentations Journals. /Resources 22 0 R /Subtype /Form /Filter /FlateDecode Google Scholar; Paul Goodwin et al. endstream Explaining and Harnessing Adversarial Examples Ian J. Goodfellow, Jonathan Schlens, and Christian Sczegedy Google Inc. Early attempts at explaining this phenomenon focused ⦠has been cited by the following article: TITLE: Malware Images Classification Using Convolutional Neural Network. /Filter /FlateDecode You are currently offline. /Length 15 Explaining and harnessing adversarial examples. %PDF-1.5 Szegedy et al first discovered that most machine learning models including the state of art deep learning models can be fooled by adversarial examples. 45 0 obj correct class ! Deep learning technology has become an important branch of artificial intelligence. /Length 1405 In Lecture 16, guest lecturer Ian Goodfellow discusses adversarial examples in deep learning. 5/41 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. /Shading << /Sh << /ShadingType 3 /ColorSpace /DeviceRGB /Domain [0 1] /Coords [4.00005 4.00005 0.0 4.00005 4.00005 4.00005] /Function << /FunctionType 2 /Domain [0 1] /C0 [0.5 0.5 0.5] /C1 [1 1 1] /N 1 >> /Extend [true false] >> >> 18 0 obj x���P(�� �� 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. << Im many cases, different ML models trained under different architecture also fell prey to these adversarial examples. This approach is also known as the Fast Gradient Sign Method (FGSM) , first proposed by Goodfellow et al. Generating adversarial examples ⢠Fast gradient sign method: Find the gradient of the loss w.r.t. stream Jia, R. and Liang, P. (2017). >> /Matrix [1 0 0 1 0 0] endstream - âDistributional Smoothing by Virtual Adversarial Examples.â Miyato et al ArXiv 2015. endobj 22 0 obj 17 0 obj (Introduction) Intriguing properties of neural networks Szegedy, Christian, et al. << /S /GoTo /D (Outline0.1) >> 6.2 Adversarial Examples. Adversarial examples p(x is panda) = 0.58 4 p(x is gibbon) = 0.99 [ICLR 15] Goodfellow, Shlens, and Szegedy. /Matrix [1 0 0 1 0 0] Explaining and Harnessing Adversarial Examples. This proves that all machine learning algorith⦠Approaches for Generating Adversarial Examples in Deep Learning. Abstract. We argue instead that the primary cause of neural networks' vulnerability to adversarial…. Benchmarks constructed without a ⦠Google Proprietary In this presentationâ¦. 2010. endobj endobj 14 0 obj Explaining and Harnessing Adversarial Examples 20 Dec 2014 ⢠Ian J. Goodfellow ⢠Jonathon Shlens ⢠Christian Szegedy 10 0 obj stream in their paper Explaining and harnessing adversarial examples [2] . Some features of the site may not work correctly. stream Adversarial examples are beginning to evolve as rapidly as the deep learning models they are designed to attack. Several machine learning models, including neural networks, consistently misclassify adversarial examplesâinputs 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. >> The holt-winters approach to exponential smoothing: 50 years old and going strong. Ian Goodfellow, Jonathon Shlens and Christian Szegedy ICLR 2015 (ICLR 2015)EXPLAINING AND HARNESSING ADVERSARIAL EXAMPLES03 April 2018 9 / 18 Linear Perturbation of Non-Linear Examples They nd that using = :25, we cause a shallow softmax classi er to have Advanced Photonics Journal of Applied Remote Sensing (2014)cite arxiv:1412.6572. /Shading << /Sh << /ShadingType 2 /ColorSpace /DeviceRGB /Domain [0.0 8.00009] /Coords [0 0.0 0 8.00009] /Function << /FunctionType 3 /Domain [0.0 8.00009] /Functions [ << /FunctionType 2 /Domain [0.0 8.00009] /C0 [1 1 1] /C1 [0.5 0.5 0.5] /N 1 >> << /FunctionType 2 /Domain [0.0 8.00009] /C0 [0.5 0.5 0.5] /C1 [0.5 0.5 0.5] /N 1 >> ] /Bounds [ 4.00005] /Encode [0 1 0 1] >> /Extend [false false] >> >> /BBox [0 0 5669.291 8] 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.This was one of the first and most popular attacks to fool a neural network. /BBox [0 0 16 16] 19 0 obj /Length 15
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