Deepfool: a simple and accurate method to fool deep neural networks
Original
S. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2015
Cited alongside, same era.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. Nguyen, J. Yosinski, and J. Clune · 2015
Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Cited alongside, same era.
Understanding adversarial training: Increasing local stability of neural nets through robust optimization
Original
U. Shaham, Y. Yamada, and S. Negahban · 2015
Cited alongside, same era.
Robustness of classifiers: from adversarial to random noise
Original
A. Fawzi, S. Moosavi-Dezfooli, and P. Frossard · 2016
Cited alongside, same era.
Adversarial examples in the physical world
Original
A. Kurakin, I. J. Goodfellow, and S. Bengio · 2016
Cited alongside, same era.
Universal adversarial perturbations
Original
S.-M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard · 2016
Cited alongside, same era.
Yolo9000: better, faster, stronger
Original
J. Redmon and A. Farhadi · 2016
Cited alongside, same era.
Defensive distillation is not robust to adversarial examples
N. Carlini and D. Wagner
Cited in the paper.