指纹深度人脸识别的样本相关性

IF 11.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jiyang Guan, Jian Liang, Yanbo Wang, Ran He
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引用次数: 0

摘要

近年来,得益于深度学习技术的发展,人脸识别技术取得了显著进步。然而,作为一种商业服务,现成的人脸识别模型可能会被模型窃取攻击窃取,对模型所有者的权益造成极大威胁。模型指纹识别作为一种模型窃取检测方法,旨在验证可疑模型是否是从受害模型中窃取的,如今正受到越来越多的关注。以往的方法总是利用可转移的对抗实例作为模型指纹,但众所周知,这种方法对对抗防御和转移学习技术很敏感。为了解决这个问题,我们考虑了样本之间的成对关系,提出了一种新颖而简单的基于 "简单相关性"(SAmple Correlation,SAC)的模型窃取检测方法。具体来说,我们提出的 SAC-JC 可以选择 JPEG 压缩样本作为模型输入,并计算其模型输出之间的相关矩阵。广泛的结果验证了 SAC 成功抵御了深度人脸识别(包括人脸验证和人脸情感识别)中的各种模型窃取攻击,在 AUC、P 值和 F1 分数方面表现出了最高的性能。此外,我们还将SAC-JC的评估扩展到了物体识别数据集,包括Tiny-ImageNet和CIFAR10,这也证明了SAC-JC的性能优于之前的方法。代码可在 https://github.com/guanjiyang/SAC_JC 上获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Sample Correlation for Fingerprinting Deep Face Recognition

Sample Correlation for Fingerprinting Deep Face Recognition

Face recognition has witnessed remarkable advancements in recent years, thanks to the development of deep learning techniques. However, an off-the-shelf face recognition model as a commercial service could be stolen by model stealing attacks, posing great threats to the rights of the model owner. Model fingerprinting, as a model stealing detection method, aims to verify whether a suspect model is stolen from the victim model, gaining more and more attention nowadays. Previous methods always utilize transferable adversarial examples as the model fingerprint, but this method is known to be sensitive to adversarial defense and transfer learning techniques. To address this issue, we consider the pairwise relationship between samples instead and propose a novel yet simple model stealing detection method based on SAmple Correlation (SAC). Specifically, we present SAC-JC that selects JPEG compressed samples as model inputs and calculates the correlation matrix among their model outputs. Extensive results validate that SAC successfully defends against various model stealing attacks in deep face recognition, encompassing face verification and face emotion recognition, exhibiting the highest performance in terms of AUC, p-value and F1 score. Furthermore, we extend our evaluation of SAC-JC to object recognition datasets including Tiny-ImageNet and CIFAR10, which also demonstrates the superior performance of SAC-JC to previous methods. The code will be available at https://github.com/guanjiyang/SAC_JC.

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来源期刊
International Journal of Computer Vision
International Journal of Computer Vision 工程技术-计算机:人工智能
CiteScore
29.80
自引率
2.10%
发文量
163
审稿时长
6 months
期刊介绍: The International Journal of Computer Vision (IJCV) serves as a platform for sharing new research findings in the rapidly growing field of computer vision. It publishes 12 issues annually and presents high-quality, original contributions to the science and engineering of computer vision. The journal encompasses various types of articles to cater to different research outputs. Regular articles, which span up to 25 journal pages, focus on significant technical advancements that are of broad interest to the field. These articles showcase substantial progress in computer vision. Short articles, limited to 10 pages, offer a swift publication path for novel research outcomes. They provide a quicker means for sharing new findings with the computer vision community. Survey articles, comprising up to 30 pages, offer critical evaluations of the current state of the art in computer vision or offer tutorial presentations of relevant topics. These articles provide comprehensive and insightful overviews of specific subject areas. In addition to technical articles, the journal also includes book reviews, position papers, and editorials by prominent scientific figures. These contributions serve to complement the technical content and provide valuable perspectives. The journal encourages authors to include supplementary material online, such as images, video sequences, data sets, and software. This additional material enhances the understanding and reproducibility of the published research. Overall, the International Journal of Computer Vision is a comprehensive publication that caters to researchers in this rapidly growing field. It covers a range of article types, offers additional online resources, and facilitates the dissemination of impactful research.
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