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Deep neural networks (DNNs) can be easily fooled by adding human imperceptible perturbations to the images.
Semantic object classes in video: A high-definition ground truth database
G. J. Brostow, J. Fauqueur, and R. Cipolla · 2009
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Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Cnn features off-the-shelf: an astounding baseline for recognition
A. Sharif Razavian, H. Azizpour, J. Sullivan, and S. Carlsson · 2014
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Very deep convolutional networks for large-scale image recognition, 2014
K. Simonyan and A. Zisserman · 2014
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2014
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Explaining and harnessing adversarial examples
I. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Adversarial machine learning at scale
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
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Synthesizing robust adversarial examples
A. Athalye, L. Engstrom, A. Ilyas, and K. Kwok · 2017
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
V. Badrinarayanan, A. Kendall, and R. Cipolla · 2017
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Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2017
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Adversarial examples in the physical world
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2017
Boosting adversarial attacks with momentum
Y. Dong, F. Liao, T. Pang, H. Su, J. Zhu, X. Hu, and J. Li · 2018
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Do better imagenet models transfer better?
S. Kornblith, J. Shlens, and Q. V. Le · 2018
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Deblurgan: Blind motion deblurring using conditional adversarial networks
O. Kupyn, V. Budzan, M. Mykhailych, D. Mishkin, and J. Matas · 2018
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Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2018
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Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2018
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Fast feature fool: A data independent approach to universal adversarial perturbations
K. R. Mopuri, U. Garg, and R. V. Babu · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
A. Athalye, N. Carlini, and D. A. Wagner · 2018
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D. Su, H. Zhang, H. Chen, J. Yi, P.-Y. Chen, and Y. Gao · 2018
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Ensemble adversarial training: Attacks and defenses
F. Tramèr, A. Kurakin, N. Papernot, D. Boneh, and P. McDaniel · 2018
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Improving transferability of adversarial examples with input diversity
C. Xie, Z. Zhang, J. Wang, Y. Zhou, Z. Ren, and A. Yuille · 2018
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Transferable adversarial perturbations
W. Zhou, X. Hou, Y. Chen, M. Tang, X. Huang, X. Gan, and Y. Yang · 2018
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