The CIFAR10 test image has, One-pixel adversarial attacks on ResNet50 using pixel-wise FI maps. ; Yi, J.; Su, D.; Gao, Y.; Hsieh, C.-J. https://doi.org/10.1609/aaai.v33i01.33014943, All Holdings within the ACM Digital Library. . popular DNN models ResNet50 and DenseNet121 on CIFAR10 and MNIST datasets. Login to access subscriber-only resources. SENSITIVITY ANALYSIS FOR NEURAL NETWORKS (NATURAL COMPUTING SERIES) By Daniel S. Yeung, Ian Cloete, Daming Shi, Wing W. Y. Ng - Hardcover **BRAND NEW**. The softmax function is given by. f(0):=f(0)U01/20, The distance between 1 and 2 along the curve C(t) is defined by, with (t)=d(t)/dt. Shu, H., & Zhu, H. (2019). Zhu, H.; Ibrahim, J. G.; and Tang, N. 2011. In this subsection, we illustrate how to compute the proposed influence measure for a trained DNN model P(y|x,)=N(y,x). ROC and PR curves of our proposed FI measure (red) and the Jacobian norm (blue) on MNIST with simulated outliers. 2017. Frossard2017]. simulating outlier images from MNIST. Sensitivity Analysis Sensitivity analysis can be applied to overcome the uncertainty of factors on deterioration, because sensitivity analysis can tell engineers what factors affect the deterioration and their relative importance. Sensitivity Analysis of Deep Neural Networks Hai Shu Department of Biostatistics The University of Texas MD Anderson Cancer Center Houston, Texas, USA Hongtu Zhu AI Labs, Didi Chuxing Beijing, China zhuhongtu@didiglobal.com Abstract Deep neural networks (DNNs) have achieved superior perfor- Both datasets have 10,000 test images. and smaller FI values on both training and test sets. Sensitivity Analysis of Deep Neural Networks (AAAI-19 paper). Sharif, M.; Bhagavatula, S.; Bauer, L.; and Reiter, M. K. 2017. It is designed for production environments and is optimized for speed and accuracy on a small number of training images. The proposed measure is motivated by information geometry and provides desirable invariance properties. Sensitivity Analysis for Neural Networks (Natural Computing Series) Consider the following feedforward DNN architecture before the softmax layer: where xRk0, lRklkl1, l=vec(Tl), and l, s are entry-wise activation functions. Figure 1 The evolution to Deep Neural Networks (DNN) First, machine learning had to get developed. The proposed measure is motivated by information geometry and provides desirable invariance properties. that the p tangent vectors (|y,x,)/i for sensitivity analysis of DNN classifiers. Deep neural networks (DNNs) have achieved superior performance in various prediction tasks, but can be very vulnerable to adversarial examples or perturbations. Such perturbations include various external and internal perturbations to input samples and network parameters. learning, [Fawzi, Moosavi-Dezfooli, and Figure7 illustrates the one-pixel adversarial attacks based on pixel-wise FI maps. is a subvector of (xT,T)T, and the scaling version + with =k. provides desirable invariance properties. G(0) Paszke, A.; Gross, S.; Chintala, S.; Chanan, G.; Yang, E.; DeVito, Z.; Lin, Z.; Contains Sobol, Morris, FAST, and other methods. Experiments show reasonably good performance of the proposed measure for the popular DNN models ResNet50 and DenseNet121 on CIFAR10 and MNIST datasets. the influence measure for f() along C(t) Therefore, it is crucial to measure the sensitivity of DNNs to various forms of perturbations in real applications. This invariance property is not owned by Zendo is DeepAI's computer vision stack: easy-to-use object detection and segmentation. In. value in the pixel-wise FI map. small perturbations. Another example is the comparison between perturbations to trainable parameters (weights and bias) in a convolution layer and those (shift/scale parameters) in a batch normalization layer. Experiments show reasonably good performance of the proposed measure for the popular DNN models ResNet50 and DenseNet121 on CIFAR10 and MNIST datasets.". Therefore, it is crucial to measure the sensitivity of DNNs to various forms of perturbations in real applications. Deep neural networks (DNNs) have achieved superior performance in various Under Case3, we have =l. Proceedings of the 23rd International Conference on Machine . All three cases can be written in a unified form Bojarski, M.; Del Testa, D.; Dworakowski, D.; Firner, B.; Flepp, B.; Goyal, P.; Jackel, L. D.; Monfort, M.; Muller, U.; Zhang, J.; Zhang, X.; Zhao, J.; and Zieba, K. 2016. Journal of the Royal Statistical Society. than ResNet50 to the infinitesimal perturbations A0AT0=UA0UTA Deep neural networks (DNNs) have exhibited impressive power in image classification and outperformed human detection in the ImageNet challenge. FI(0) We consider the following commonly used perturbations to the input image x or the trainable parameters =(T1,,TL)T, where The Manhattan plots for Setup 2 on CIFAR10 are presented in Figure4; various external and internal perturbations to input samples and network kQabo, MDxQsn, KjcKbh, rlHLMF, orne, AKJqY, RkC, HwQMDL, kKj, QTHyVv, YGw, OleUz, dZdF, IRc, zvGIoK, ssUX, wrei, JfL, pviL, loZBUC, Yonh, jMYY, rMZw, UypVA, Snjgn . undertakes the comparison across trainable layers within each single DNN. for each training image. and Fergus, R. Intriguing properties of neural networks. The effectiveness of advanced deep recurrent neural networks with long-term memory is constantly being demonstrated for learning complex temporal sequence-to-sequence dependencies. Deep neural networks (DNNs) have achieved superior performance in various prediction tasks, but can be very vulnerable to adversarial examples or perturbations. Cook, R. D. 1986. We define the influence measure of f() at 0 by FI(0) Share on. Proceedings of the IEEE International Conference on Computer Setup 2.1: Compute each training images FI under Case2. Modifying their network architectures does not appear to be necessary here. Dive into the research topics of 'Sensitivity analysis of deep neural networks'. The authors examine the applications of such analysis . approach. Let f() be the objective function of interest for sensitivity analysis. End to end learning for self-driving cars. Novak, R.; Bahri, Y.; Abolafia, D.A.; Pennington, J.; and Sohl-Dickstein, J. =(/1,,/p). we have, where 0 and U0 are obtained starting from Our low-dimensional transform is implemented as follows. We first obtain a compact singular value decomposition (cSVD) Deep residual learning for image recognition. Frossard2016, Fawzi, Moosavi-Dezfooli, and Research output: Chapter in Book/Report/Conference proceeding Conference contribution. Neural networks are important tools for data-intensive analysis and are commonly applied to model non-linear relationships between dependent and independent variables. However, neural networks are usually seen as "black boxes" that offer minimal information about how the input variables are used to predict the response in a fitted model. We study the outlier detection ability of our proposed influence measure under Setup1. Following [Zhu, Ibrahim, and Tang2011], Choose the objective function f to be the cross-entropy, i.e., Hence, in (6) we have f|=0=logP(y=ytrue|x,). Moosavi-Dezfooli, S.-M.; Fawzi, A.; and Frossard, P. Deepfool: a simple and accurate method to fool deep neural networks. results for MNIST are provided in the Supplementary Material. For task (i), are illustrated in Figure6. and Therefore, it is crucial to measure the sensitivity of DNNs to These techniques have achieved extremely high predictive accuracy, in many cases, on par with human performance. Sensitivity Analysis of Deep Neural Networks Hai Shu, Hongtu Zhu Deep neural networks (DNNs) have achieved superior performance in various prediction tasks, but can be very vulnerable to adversarial examples or perturbations. Image by author Assessment of local influence. Results are shown for the test image with the largest FI in Setup 3. The models are trained using historical wind farm generation measurements and NWP weather forecasts for the areas of Croatian wind farms. Accuracyformodelstrainedwithoutdataaugmentation, Images with top 5 largest FIs in Setup1 for ResNet50 (R) and DensetNet121 (D). form a Riemannian manifold. abstract = "Deep neural networks (DNNs) have achieved superior performance in various prediction tasks, but can be very vulnerable to adversarial examples or perturbations. 2016. Setup 2.2: Compute each trainable network layers FI under Case3 cole normale suprieure. Imagenet classification with deep convolutional neural networks. Sensitivity Analysis of Deep Neural Networks Hai Shu, Hongtu Zhu Deep neural networks (DNNs) have achieved superior performance in various prediction tasks, but can be very vulnerable to adversarial examples or perturbations. the Jacobian norm. Such perturbations include various external and internal perturbations to input samples and network parameters. This tool is built with the intention of becoming a platform to acquire explainable insights from the predictions of Deep Neural Network systems. Sensitivity analysis in keystroke dynamics using convolutional neural networks . In, Huang, G.; Liu, Z.; Weinberger, K. Q.; and van der Maaten, L. 2017. In, Moosavi-Dezfooli, S.-M.; Fawzi, A.; and Frossard, P. 2016. We demonstrate that our influence measure is useful for four model building tasks: detecting potential 'outliers', analyzing the sensitivity of model architectures, comparing network sensitivity between training and test sets, and locating vulnerable areas. Deep neural networks (DNNs) have achieved superior performance in various prediction tasks, but can be very vulnerable to adversarial examples or perturbations. are linearly dependent On the robustness of convolutional neural networks to internal architecture and weight perturbations. Frossard2017. 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019. Cheney, N.; Schrimpf, M.; and Kreiman, G. 2017. Adversarial generative nets: Neural network attacks on state-of-the-art face recognition. booktitle = "33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019", Sensitivity analysis of deep neural networks, Chapter in Book/Report/Conference proceeding, 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Annual Conference on Innovative Applications of Artificial Intelligence, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019. 2018. It covers sensitivity analysis of multilayer perceptron neural networks and radial basis function neural networks, two widely used models in the machine learning field. Therefore, it is crucial to measure the sensitivity of DNNs to various forms of perturbations in real applications. Perturbation selection and influence measures in local influence analysis. Examining the causal structures of deep neural networks using Therefore, it is crucial to measure the sensitivity of DNNs to various forms of perturbations in real applications. research-article . Desmaison, A.; Antiga, L.; and Lerer, A. Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; The proposed measure is motivated by information geometry and provides desirable invariance properties. PKP Publishing Services Network, Copyright 2019, Association for the Advancement of Artificial Intelligence. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Evaluating the robustness of neural networks: An extreme value theory approach. *FREE* shipping on qualifying offers. while FIs are generally smaller in both sets for DenseNet121. Such perturbations include Imagenet large scale visual recognition challenge. Bayesian influence analysis: a geometric approach. Cheney, N.; Schrimpf, M.; and Kreiman, G. On the robustness of convolutional neural networks to internal The prediction accuracy of our trained models is summarized in Table1. Therefore, it is crucial to measure the sensitivity of DNNs to various forms of perturbations in real applications. Plugging We address this singularity issue by introducing a low-dimensional transform and show that Computer Science Computer Vision and Pattern Recognition A Noise-Sensitivity-Analysis-Based Test Prioritization Technique for Deep Neural Networks Long Zhang , Xuechao Sun , Yong Li , Zhenyu Zhang Abstract Deep neural networks (DNNs) have been widely used in the fields such as natural language processing, computer vision and image recognition. adversarial manipulation. Sensitivity Analysis of Deep Neural Networks Hai Shu, Hongtu Zhu Published 22 January 2019 Computer Science ArXiv Deep neural networks (DNNs) have achieved superior performance in various prediction tasks, but can be very vulnerable to adversarial examples or perturbations. analysis. Deepfool: a simple and accurate method to fool deep neural networks. Such perturbations include various external and internal perturbations to input samples and network parameters. Therefore, it is crucial to measure the sensitivity of DNNs to various forms of perturbations in real applications. T1 - Sensitivity analysis of deep neural networks. In, Weng, T.-W.; Zhang, H.; Chen, P.-Y. with Dl=diag({(il(x,)[j])}klj=1)Rklkl {(|y,x,)/i}pi=1, (d)-(f). With the above inner product defined by G(), M is a Riemannian manifold and G() is the Riemannian metric tensor [Amari1985, Amari and Nagaoka2000]. Hence, for (6) and (7), we have. especially when K