arXiv cs.AI· Quentin Le Roux, Marti Jimenez, Yannick Teglia, Philippe Loubet Moundi·· 11 小时前AI 评分31
防御深度学习人脸识别流水线后门攻击的一套实用技巧
A Bag of Tricks for Defending Deep Learning-based Face Recognition Pipelines against Backdoor Attacks
AI 导读
针对训练数据来自可能被攻破的第三方、而模型训练在内部完成这一威胁模型，一份技术报告提出三种防御数据投毒后门攻击的实用技巧。第一种在训练时检测被投毒的身份，另两种在推理时过滤可疑输入，且每种方法都给出了动机、算法与计算开销，并在真实人脸识别工作负载上评估。
正文 · 原文
Abstract:The protection of mature, multi-stage Face Recognition Systems is crucial to system providers. This technical report targets the threat model most relevant to industry practitioners: although model training is performed in-house, training data is sourced from possibly-compromised third parties. Given this setting, we present three practical defense "tricks" against data poisoning-based backdoor attacks. The first operates at training-time, detecting poisoned identities during training or prior to a pipeline's deployment. The last two operate at inference-time, filtering suspicious inputs before an answer can be sent back to a potential attacker. Each method is presented with their motivation, algorithms and computational impact, and evaluated on realistic face recognition workloads. Our goal is to provide actionable hardening techniques to system providers.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG) |
| Cite as: | arXiv:2611.06412 [cs.CV] |
| (or arXiv:2611.06412v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06412 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Quentin Le Roux [view email]
[v1]
Wed, 4 Nov 2026 12:12:32 UTC (1,132 KB)
来源：arXiv cs.AI · arxiv.org