On the Difficulty of Defending Contrastive Learning against Backdoor Attacks

14 December 2023

Papers citing "On the Difficulty of Defending Contrastive Learning against Backdoor Attacks"

8 / 8 papers shown

Title
DeBackdoor: A Deductive Framework for Detecting Backdoor Attacks on Deep Models with Limited Data Dorde Popovic Amin Sadeghi Ting Yu Sanjay Chawla Issa M. Khalil AAML 46 0 0 27 Mar 2025
DeDe: Detecting Backdoor Samples for SSL Encoders via Decoders Sizai Hou Songze Li Duanyi Yao AAML 70 0 0 25 Nov 2024
Protecting against simultaneous data poisoning attacks Neel Alex Shoaib Ahmed Siddiqui Amartya Sanyal David M. Krueger AAML 35 1 0 23 Aug 2024
A Wolf in Sheep's Clothing: Practical Black-box Adversarial Attacks for Evading Learning-based Windows Malware Detection in the Wild Xiang Ling Zhiyu Wu Bin Wang Wei Deng Jingzheng Wu Shouling Ji Tianyue Luo Yanjun Wu AAML 29 1 0 03 Jul 2024
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 258 7,412 0 11 Nov 2021
Improved Baselines with Momentum Contrastive Learning Xinlei Chen Haoqi Fan Ross B. Girshick Kaiming He SSL 238 3,359 0 09 Mar 2020
Clean-Label Backdoor Attacks on Video Recognition Models Shihao Zhao Xingjun Ma Xiang Zheng James Bailey Jingjing Chen Yu-Gang Jiang AAML 185 274 0 06 Mar 2020
Model-Reuse Attacks on Deep Learning Systems Yujie Ji Xinyang Zhang S. Ji Xiapu Luo Ting Wang SILM AAML 123 186 0 02 Dec 2018