Security for Machine Learning-based Systems: Attacks and Challenges during Training and Inference

5 November 2018

Papers citing "Security for Machine Learning-based Systems: Attacks and Challenges during Training and Inference"

6 / 6 papers shown

Title
Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead Mohamed Bennai Mahum Naseer T. Theocharides C. Kyrkou O. Mutlu Lois Orosa Jungwook Choi OOD 81 100 0 04 Jan 2021
Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead Maurizio Capra Beatrice Bussolino Alberto Marchisio Guido Masera Maurizio Martina Mohamed Bennai BDL 59 140 0 21 Dec 2020
QuSecNets: Quantization-based Defense Mechanism for Securing Deep Neural Network against Adversarial Attacks Faiq Khalid Hassan Ali Hammad Tariq Muhammad Abdullah Hanif Semeen Rehman Rehan Ahmed Mohamed Bennai AAML MQ 35 37 0 04 Nov 2018
DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks D. Gopinath Guy Katz C. Păsăreanu Clark W. Barrett AAML 50 87 0 02 Oct 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 251 1,842 0 03 Feb 2017
Safety Verification of Deep Neural Networks Xiaowei Huang Marta Kwiatkowska Sen Wang Min Wu AAML 183 933 0 21 Oct 2016