On the Effectiveness of Defensive Distillation

18 July 2016

Papers citing "On the Effectiveness of Defensive Distillation"

13 / 13 papers shown

Title
On the Robustness of Kolmogorov-Arnold Networks: An Adversarial Perspective Tal Alter Raz Lapid Moshe Sipper AAML 62 6 0 25 Aug 2024
Survey: Leakage and Privacy at Inference Time Marija Jegorova Chaitanya Kaul Charlie Mayor Alison Q. OÑeil Alexander Weir Roderick Murray-Smith Sotirios A. Tsaftaris PILM MIACV 30 71 0 04 Jul 2021
Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS Felix O. Olowononi D. Rawat Chunmei Liu 38 134 0 14 Feb 2021
DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learning Olakunle Ibitoye M. O. Shafiq Ashraf Matrawy FedML 28 18 0 08 Jan 2021
Ensemble Generative Cleaning with Feedback Loops for Defending Adversarial Attacks Jianhe Yuan Zhihai He AAML 32 22 0 23 Apr 2020
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness Ahmadreza Jeddi M. Shafiee Michelle Karg C. Scharfenberger A. Wong OOD AAML 72 63 0 02 Mar 2020
Adversarial Ranking Attack and Defense Mo Zhou Zhenxing Niu Le Wang Qilin Zhang G. Hua 36 38 0 26 Feb 2020
Adversarial Robustness via Label-Smoothing Morgane Goibert Elvis Dohmatob AAML 10 18 0 27 Jun 2019
Certifiably Robust Interpretation in Deep Learning Alexander Levine Sahil Singla S. Feizi FAtt AAML 31 63 0 28 May 2019
PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks Jan Svoboda Jonathan Masci Federico Monti M. Bronstein Leonidas J. Guibas AAML GNN 33 41 0 31 May 2018
Stochastic Activation Pruning for Robust Adversarial Defense Guneet Singh Dhillon Kamyar Azizzadenesheli Zachary Chase Lipton Jeremy Bernstein Jean Kossaifi Aran Khanna Anima Anandkumar AAML 33 545 0 05 Mar 2018
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 92 11,884 0 19 Jun 2017
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 89 8,465 0 16 Aug 2016