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We investigate conditions under which test statistics exist that can reliably detect examples, which have been adversarially manipulated in a white-box attack.
Robust control of robotic manipulators
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
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Evasion attacks against machine learning at test time
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Intriguing properties of neural networks
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
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Very deep convolutional networks for large-scale image recognition
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
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Adversarial manipulation of deep representations
Sabour, S., Cao, Y., Faghri, F., and Fleet, D. J · 2015
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Kurakin, A., Goodfellow, I., and Bengio, S · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
Moosavi Dezfooli, S. M., Fawzi, A., and Frossard, P · 2016
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Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
Papernot, N., McDaniel, P., and Goodfellow, I · 2016
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Synthesizing robust adversarial examples
Athalye, A. and Sutskever, I · 2017
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Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
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On detecting adversarial perturbations
Metzen, J. H., Genewein, T., Fischer, V., and Bischoff, B · 2017
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Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Song, Y., Kim, T., Nowozin, S., Ermon, S., and Kushman, N · 2017
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Tramèr, F., Papernot, N., Goodfellow, I., Boneh, D., and McDaniel, P · 2017
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Feinman, R., Curtin, R. R., Shintre, S., and Gardner, A. B · 2017
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On the (statistical) detection of adversarial examples
Grosse, K., Manoharan, P., Papernot, N., Backes, M., and McDaniel, P · 2017
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The best defense is a good offense: Countering black box attacks by predicting slightly wrong labels
Kilcher, Y. and Hofmann, T · 2017
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Adversarial examples detection in deep networks with convolutional filter statistics
Li, X. and Li, F · 2017
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Safetynet: Detecting and rejecting adversarial examples robustly
Lu, J., Issaranon, T., and Forsyth, D. A · 2017
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Adversarial examples are not easily detected: Bypassing ten detection methods
Carlini, N. and Wagner, D
Cited in the paper.
Xie, C., Wang, J., Zhang, Z., Ren, Z., and Yuille, A · 2017
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Feature squeezing: Detecting adversarial examples in deep neural networks
Xu, W., Evans, D., and Qi, Y · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D · 2018
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Adversarial vulnerability for any classifier
Fawzi, A., Fawzi, H., and Fawzi, O · 2018
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Gilmer, J., Metz, L., Faghri, F., Schoenholz, S. S., Raghu, M., Wattenberg, M., and Goodfellow, I · 2018
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Adversarially robust generalization requires more data
Schmidt, L., Santurkar, S., Tsipras, D., Talwar, K., and Madry, A · 2018
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