Security Matters: A Survey on Adversarial Machine Learning

16 October 2018

Papers citing "Security Matters: A Survey on Adversarial Machine Learning"

8 / 8 papers shown

Title
On the Robustness of Explanations of Deep Neural Network Models: A Survey Amlan Jyoti Karthik Balaji Ganesh Manoj Gayala Nandita Lakshmi Tunuguntla Sandesh Kamath V. Balasubramanian XAI FAtt AAML 32 4 0 09 Nov 2022
Problem-Space Evasion Attacks in the Android OS: a Survey Harel Berger Chen Hajaj A. Dvir 23 2 0 29 May 2022
What Clinical Trials Can Teach Us about the Development of More Resilient AI for Cybersecurity Edmon Begoli Robert A. Bridges Sean Oesch Kathryn Knight 14 1 0 13 May 2021
Exacerbating Algorithmic Bias through Fairness Attacks Ninareh Mehrabi Muhammad Naveed Fred Morstatter Aram Galstyan AAML 28 67 0 16 Dec 2020
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 240 1,837 0 03 Feb 2017
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 288 3,110 0 04 Nov 2016
Safety Verification of Deep Neural Networks Xiaowei Huang M. Kwiatkowska Sen Wang Min Wu AAML 180 932 0 21 Oct 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 287 5,837 0 08 Jul 2016