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The prediction accuracy has been the long-lasting and sole standard for comparing the performance of different image classification models, including the ImageNet competition.
Wordnet: a lexical database for english
Miller, G.A.: · 1995
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A.: · 2009
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: · 2012
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: · 2014
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Network in network
Lin, M., Chen, Q., Yan, S.: · 2014
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M.S., Berg, A.C., Li, F.: · 2015
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Explaining and harnessing adversarial examples
Goodfellow, I., Shlens, J., Szegedy, C.: · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2015
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S.E., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: · 2016
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Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
Papernot, N., McDaniel, P., Goodfellow, I.: · 2016
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Deepfool: A simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, S., Fawzi, A., Frossard, P.: · 2016
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Universal adversarial perturbations against semantic image segmentation
Metzen, J.H., Kumar, M.C., Brox, T., Fischer, V.: · 2017
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Formal guarantees on the robustness of a classifier against adversarial manipulation
Hein, M., Andriushchenko, M.: · 2017
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Stock, P., Cisse, M.: · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: · 2017
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Audio adversarial examples: Targeted attacks on speech-to-text
Carlini, N., Wagner, D.: · 2018
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Identify susceptible locations in medical records via adversarial attacks on deep predictive models
Sun, M., Tang, F., Yi, J., Wang, F., Zhou, J.: · 2018
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Generating adversarial examples with adversarial networks
Xiao, C., Li, B., yan Zhu, J., He, W., Liu, M., Song, D.: · 2018
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Spatially transformed adversarial examples
Xiao, C., Zhu, J.Y., Li, B., He, W., Liu, M., Song, D.: · 2018
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Robust physical-world attacks on deep learning visual classification
Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., Song, D.: · 2018
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Evaluating the robustness of neural networks: An extreme value theory approach
Weng, T.W., Zhang, H., Chen, P.Y., Yi, J., Su, D., Gao, Y., Hsieh, C.J., Daniel, L.: · 2018
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Neural architecture search with reinforcement learning
Zoph, B., Le, Q.V.: · 2017
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Tensorflow-Slim Image Classification Model Library
Wu, N., Sivakumar, S., Guadarrama, S., Andersen, D.: · 2017
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Delving into transferable adversarial examples and black-box attacks
Liu, Y., Chen, X., Liu, C., Song, D.: · 2017
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Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Chen, P.Y., Zhang, H., Sharma, Y., Yi, J., Hsieh, C.J.: · 2017
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Adversarial machine learning at scale
Kurakin, A., Goodfellow, I.J., Bengio, S.: · 2017
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Parseval networks: Improving robustness to adversarial examples
Cisse, M., Bojanowski, P., Grave, E., Dauphin, Y., Usunier, N.: · 2017
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Towards evaluating the robustness of neural networks
Carlini, N., Wagner, D.A.: · 2017
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Towards fast computation of certified robustness for relu networks
Weng, T.W., Zhang, H., Chen, H., Song, Z., Hsieh, C.J., Boning, D., Dhillon, I.S., Daniel, L.: · 2018
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Learning transferable architectures for scalable image recognition
Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: · 2018
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Autozoom: Autoencoder-based zeroth order optimization method for attacking black-box neural networks
Tu, C., Ting, P., Chen, P., Liu, S., Zhang, H., Yi, J., Hsieh, C., Cheng, S.: · 2018
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Query-efficient hard-label black-box attack: An optimization-based approach
Cheng, M., Le, T., Chen, P.Y., Yi, J., Zhang, H., Hsieh, C.J.: · 2018
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Autozoom: Autoencoder-based zeroth order optimization method for attacking black-box neural networks
Tu, C.C., Ting, P., Chen, P.Y., Liu, S., Zhang, H., Yi, J., Hsieh, C.J., Cheng, S.M.: · 2018
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Ead: Elastic-net attacks to deep neural networks via adversarial examples
Chen, P.Y., Sharma, Y., Zhang, H., Yi, J., Hsieh, C.J.: · 2018
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On the limitation of magnet defense against L 1 {L_{1}} -based adversarial examples
Lu, P.H., Chen, P.Y., Chen, K.C., Yu, C.M.: · 2018
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On the limitation of local intrinsic dimensionality for characterizing the subspaces of adversarial examples
Lu, P.H., Chen, P.Y., Yu, C.M.: · 2018
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: · 2018
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Synthesizing robust adversarial examples
Athalye, A., Engstrom, L., Ilyas, A., Kwok, K.: · 2018
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Adversarial examples from computational constraints
Bubeck, S., Price, E., Razenshteyn, I.: · 2018
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Robustness may be at odds with accuracy
Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., Madry, A.: · 2019
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