Let the Noise Speak: Harnessing Noise for a Unified Defense Against Adversarial and Backdoor Attacks
Authors: Md Hasan Shahriar, Ning Wang, Naren Ramakrishnan, Y. Thomas Hou, Wenjing Lou | Published: 2024-06-18 | Updated: 2025-04-14
再構成攻撃
Reconstructing Training Data with Informed Adversaries
Authors: Borja Balle, Giovanni Cherubin, Jamie Hayes | Published: 2022-01-13 | Updated: 2022-04-25
Understanding Training-Data Leakage from Gradients in Neural Networks for Image Classification
Authors: Cangxiong Chen, Neill D. F. Campbell | Published: 2021-11-19
Multiple Classification with Split Learning
Authors: Jongwon Kim, Sungho Shin, Yeonguk Yu, Junseok Lee, Kyoobin Lee | Published: 2020-08-22 | Updated: 2020-09-11
The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks
Authors: Yuheng Zhang, Ruoxi Jia, Hengzhi Pei, Wenxiao Wang, Bo Li, Dawn Song | Published: 2019-11-17 | Updated: 2020-04-18
Reconstruction of Privacy-Sensitive Data from Protected Templates
Authors: Shideh Rezaeifar, Behrooz Razeghi, Olga Taran, Taras Holotyak, Slava Voloshynovskiy | Published: 2019-05-08
Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning
Authors: Ahmed Salem, Apratim Bhattacharya, Michael Backes, Mario Fritz, Yang Zhang | Published: 2019-04-01 | Updated: 2019-11-30