A Systematic Review on Model Watermarking for Neural Networks
Authors: Franziska Boenisch | Published: 2020-09-25 | Updated: 2021-12-08
Membership Inference
Fine-tuning Is Not Enough: A Simple yet Effective Watermark Removal Attack for DNN Models
Authors: Shangwei Guo, Tianwei Zhang, Han Qiu, Yi Zeng, Tao Xiang, Yang Liu | Published: 2020-09-18 | Updated: 2021-05-17
On Primes, Log-Loss Scores and (No) Privacy
Authors: Abhinav Aggarwal, Zekun Xu, Oluwaseyi Feyisetan, Nathanael Teissier | Published: 2020-09-17
An Extension of Fano’s Inequality for Characterizing Model Susceptibility to Membership Inference Attacks
Authors: Sumit Kumar Jha, Susmit Jha, Rickard Ewetz, Sunny Raj, Alvaro Velasquez, Laura L. Pullum, Ananthram Swami | Published: 2020-09-17
Manifold attack
Authors: Khanh-Hung Tran, Fred-Maurice Ngole-Mboula, Jean-Luc Starck | Published: 2020-09-13 | Updated: 2021-03-17
Quantifying Membership Inference Vulnerability via Generalization Gap and Other Model Metrics
Authors: Jason W. Bentley, Daniel Gibney, Gary Hoppenworth, Sumit Kumar Jha | Published: 2020-09-11
Privacy Analysis of Deep Learning in the Wild: Membership Inference Attacks against Transfer Learning
Authors: Yang Zou, Zhikun Zhang, Michael Backes, Yang Zhang | Published: 2020-09-10
CLEANN: Accelerated Trojan Shield for Embedded Neural Networks
Authors: Mojan Javaheripi, Mohammad Samragh, Gregory Fields, Tara Javidi, Farinaz Koushanfar | Published: 2020-09-04
Adversarial Privacy Preserving Graph Embedding against Inference Attack
Authors: Kaiyang Li, Guangchun Luo, Yang Ye, Wei Li, Shihao Ji, Zhipeng Cai | Published: 2020-08-30
Against Membership Inference Attack: Pruning is All You Need
Authors: Yijue Wang, Chenghong Wang, Zigeng Wang, Shanglin Zhou, Hang Liu, Jinbo Bi, Caiwen Ding, Sanguthevar Rajasekaran | Published: 2020-08-28 | Updated: 2021-07-04