From the 2 of 5 linked papers with an AI index.
6 papers
Benign on Label, Malicious by Design: Clean-Label Dormant-to-Activated Backdoor via Machine Unlearning with Removable Camouflage
Dongdong Zhao, Can Li, Xiang Yao +3
The paper proposes a clean‑label backdoor attack that stays dormant during training and becomes active only after specific camouflage samples are removed via machine unlearning, us…
From Classification to Consistent Templates: Multiple Permuted-Label Classifier Encoding for Biometric Template Protection
Baogang Song, Zhongshu Zhao, Qianrong Zheng +2
The paper introduces Multiple Permuted-Label Classifier Encoding (MPLCE), a method that converts identity classification outputs into randomized, hashed biometric templates, enabli…
Discard the Dross and Select the Essential: Pre-query Sample Selection for Black-box Membership Inference Attacks
Dongdong Zhao, Jinrong Hu, Changtian Song +3
Black-box membership inference attacks (MIAs) rely on target-model queries to infer whether candidate samples were used for training. However, membership signals are highly non-uni…
Model Inversion Attack Against Deep Hashing
Dongdong Zhao, Qiben Xu, Ranxin Fang +1
Deep hashing improves retrieval efficiency through compact binary codes, yet it introduces severe and often overlooked privacy risks. The ability to reconstruct original training d…
Injection, Attack and Erasure: Revocable Backdoor Attacks via Machine Unlearning
Baogang Song, Dongdong Zhao, Jianwen Xiang +2
Backdoor attacks pose a persistent security risk to deep neural networks (DNNs) due to their stealth and durability. While recent research has explored leveraging model unlearning…
BioDeepHash: Mapping Biometrics into a Stable Code
Baogang Song, Dongdong Zhao, Jiang Yan +2
With the wide application of biometrics, more and more attention has been paid to the security of biometric templates. However most of existing biometric template protection (BTP)…