Benign on Label, Malicious by Design: Clean-Label Dormant-to-Activated Backdoor via Machine Unlearning with Removable Camouflage

arXiv:2607.27936 · cs.CR · Submitted 2026-07-30 · Read on arXiv

Dongdong Zhao, Can Li, Xiang Yao, Fan He, Qihang Ge, Baogang Song

cs.CR

Submitted: 2026-07-30

Comments: 12 pages, 7 figures, 4 tables;

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Machine Unlearning — 18× in this paper · explained in 2 episodes
Consistency — 7× in this paper · explained in 3 episodes
Pruning — 6× in this paper · explained in 4 episodes
Accuracy — 5× in this paper · explained in 1 episode
Verification — 4× in this paper · explained in 2 episodes
Ablation — 3× in this paper · explained in 1 episode
Attack Success Rate — 3× in this paper
Transferability — 3× in this paper · explained in 4 episodes
Adaptation — 2× in this paper · explained in 1 episode
Approximate Unlearning — 2× in this paper · explained in 1 episode
Benign Accuracy — 2× in this paper
Core — 2× in this paper · explained in 2 episodes
Robustness — 2× in this paper · explained in 5 episodes
Ablation Studies — 1× in this paper · explained in 1 episode
Ablation study — 1× in this paper · explained in 1 episode
Aggregation — 1× in this paper · explained in 1 episode
Attack Success Rate (ASR) — 1× in this paper · explained in 2 episodes
Benchmark — 1× in this paper · explained in 2 episodes
Cosine Similarity — 1× in this paper · explained in 3 episodes
Data Augmentation — 1× in this paper · explained in 7 episodes
Influence Functions — 1× in this paper · explained in 2 episodes
Provenance — 1× in this paper · explained in 4 episodes

Sources

Related papers