Adversarial Perturbations

Y. Cheng, I. Diakonikolas, D. Kane, A. Stewart, “Robust Learning of Fixed-Structure Bayesian Networks”, NIPS 2018, 2018.

S. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, P. Frossard, “Universal adversarial perturbations”, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, July 2017.

S. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, P. Frossard, S. Soatto, "Analysis of universal adversarial perturbations”, Preprint, 2017.

K. Mopuri, U. Garg, R. Babu, “Fast feature fool: A data independent approach to universal adversarial perturbations”, British Machine Vision Conference, BMVC 2017, 2017.

K. Mopuri, U. Ojha, U. Garg, R. Babu, “NAG: Network for Adversary Generation”, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, pages 742-751, 2018.

C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, R. Fergus, “Intriguing properties of neural networks, International Conference on Learning Representations, 2014.

Z. Zheng, P. Hong, “Robust Detection of Adversarial Attacks by Modeling the Intrinsic Properties of Deep Neural Networks”, NIPS 2018, 2018.

P. Samangouei, M. Kabkab, R. Chellappa, "Defense-GAN: Protecting classiers against adversarial attacks using generative models", International Conference on Learning Representations, ICLR 2018, 2018.

A. Madry, A. Makelov, L. Schmidt, D. Tsipras, A. Vladu, "Towards deep learning models resistant to adversarial attacks", International Conference on Learning Representations, ICLR 2018, 2018.

A. Shafahi, M. Najibi, A. Ghiasi, Z. Xu, J. Dickerson, C. Studer, L. S. Davis, G. Taylor, T. Goldstein, "Adversarial training for free!", International Conference on Neural Information Processing Systems, NeurIPS 2019, pages 3358-3369, 2019.

A. Mustafa, S. Khan, M. Hayat, R. Goecke, J. Shen, L. Shao, "Deeply supervised discriminative learning for adversarial defense", IEEE transactions on Pattern Analysis and Machine Intelligence, 2020.

C. Mao, Z. Zhong, J. Yang, C. Vondrick, B. Ray, "Metric learning for adversarial robustness", International Conference onNeural Information Processing Systems, NeurIPS 2019, pages 480-491, 2019.

H. Xu, Y. Ma, H. Liu, D. Deb, H. Liu, J. Tang, A. Jain, "Adversarial attacks and defenses in images, graphs and text: A review", International Journal of Automation and Computing 151-178, 2020.

T. Pang, K. Xu, C. Du, N. Chen, J. Zhu, "Improving adversarial robustness via promoting ensemble diversity", International Conference on Machine Learning, PMLR 2019, pages 4970-4979, 2019.

A. Ross, F. Doshi-Velez, "Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients", AAAI conference on Articial Intelligence, 2018.

P. Ghosh, A. Losalka, M. Black, "Resisting Adversarial Attacks using Gaussian Mixture Variational Autoencoders", AAAI Conference on Artificial Intelligence, 2019.

A. Chan, Y. Tay, Y. Ong, J. Fu, "Jacobian Adversarially Regularized Networks for Robustness", International Conference on Learning Representations, ICLR 2020, 2020.

G. Cazenavette, C. Murdock, S. Lucey, "Architectural Adversarial Robustness: The Case for Deep Pursuit", IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, 2021.

M. Meng, G. Bai, S. Teo, Z. Hou, Y. Xiao, Y. Lin, J. Dong, "Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective", IEEE Transactions on Dependable and Secure Computing, 2022.