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Neural networks are known to be vulnerable to adversarial examples.
Imagenet: A large-scale hierarchical image database
Deng, Jia, Dong, Wei, Socher, Richard, Li, Li-Jia, Li, Kai, and Fei-Fei, Li · 2009
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, Christian, Zaremba, Wojciech, Sutskever, Ilya, Bruna, Joan, Erhan, Dumitru, Goodfellow, Ian, and Fergus, Rob · 2013
Earlier work this paper cites.
Adversarial examples are not easily detected: Bypassing ten detection methods
Carlini, Nicholas and Wagner, David · 2017
Earlier work this paper cites.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, Anish, Carlini, Nicholas, and Wagner, David · 2018
Cited alongside, same era.
Defense against adversarial attacks using high-level representation guided denoiser
Liao, Fangzhou, Liang, Ming, Dong, Yinpeng, Pang, Tianyu, Zhu, Jun, and Hu, Xiaolin · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, Aleksander, Makelov, Aleksandar, Schmidt, Ludwig, Tsipras, Dimitris, and Vladu, Adrian · 2018
Cited alongside, same era.
Deflecting adversarial attacks with pixel deflection
Prakash, Aaditya, Moran, Nick, Garber, Solomon, DiLillo, Antonella, and Storer, James · 2018
Closest in time.
Adversarial risk and the dangers of evaluating against weak attacks
Uesato, Jonathan, O’Donoghue, Brendan, Oord, Aaron van den, and Kohli, Pushmeet · 2018
Closest in time.
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