SecurityNet: Assessing Machine Learning Vulnerabilities on Public Models

SecurityNet: Assessing Machine Learning Vulnerabilities on Public Models

19 October 2023

Zheng Li

Michael Backes

Mario Fritz

Papers citing "SecurityNet: Assessing Machine Learning Vulnerabilities on Public Models"

6 / 6 papers shown

Title
SeqMIA: Sequential-Metric Based Membership Inference Attack Hao Li Zheng Li Siyuan Wu Chengrui Hu Yutong Ye Min Zhang Dengguo Feng Yang Zhang 17 3 0 21 Jul 2024
Amplifying Membership Exposure via Data Poisoning Yufei Chen Chao Shen Yun Shen Cong Wang Yang Zhang AAML 37 19 0 01 Nov 2022
Model Zoos: A Dataset of Diverse Populations of Neural Network Models Konstantin Schurholt Diyar Taskiran Boris Knyazev Xavier Giró-i-Nieto Damian Borth 39 29 0 29 Sep 2022
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 207 668 0 19 Oct 2020
Systematic Evaluation of Privacy Risks of Machine Learning Models Liwei Song Prateek Mittal MIACV 168 283 0 24 Mar 2020
Cryptanalytic Extraction of Neural Network Models Nicholas Carlini Matthew Jagielski Ilya Mironov FedML MLAU MIACV AAML 62 120 0 10 Mar 2020