Scholarlink

<p>Though the widespread application of face recognition systems facilitates user authentication and identification, it may result in potential changes in user privacy concerns. This is mainly because untrustworthy service providers utilize advanced deep learning models to automatically extract users’ soft-biometric attributes without user consent, thereby posing significant privacy threats. Existing attribute privacy protection methods can obscure multi-attribute while preserving identity information, but they fail to adjust the level of privacy protection, so they have limited flexibility in various scenarios. To solve this problem, this paper proposes an attribute disentanglement network (ADNet) to generate perturbed images using attribute codes from the attribute disentanglement module (ADM), thereby obfuscating arbitrary classifiers while preserving identity recognition. Specifically, the designed ADM can separate attribute-related and attribute-unrelated codes from attribute codes, realizing privacy protection during the transformation of attribute-related code and preserving unrelated information. Moreover, a control factor <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11227_2025_7134_Article_IEq1.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="14" /> </InlineMediaObject> <EquationSource Format="TEX">\(\alpha\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>α</mi> </math></EquationSource> </InlineEquation> is used to adjust the degree of transformation, and its value can be adjusted to meet various requirements. Extensive experimental results indicate that ADNet provides controllability for multi-attribute privacy protection while maintaining identity recognition utility. It meets the demands of different protection scenarios and outperforms previous soft-biometric privacy protection strategies.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Controllable face soft-biometric privacy enhancement based on attribute disentanglement

  • 期刊论文

  • 发表日期：

    2025年3月11日

查看全文
  • Weidi Huang,
  • Zhiqiang Yao,
  • Biao Jin,
  • Zheyu Chen,
  • Yue Wang

摘要

Though the widespread application of face recognition systems facilitates user authentication and identification, it may result in potential changes in user privacy concerns. This is mainly because untrustworthy service providers utilize advanced deep learning models to automatically extract users’ soft-biometric attributes without user consent, thereby posing significant privacy threats. Existing attribute privacy protection methods can obscure multi-attribute while preserving identity information, but they fail to adjust the level of privacy protection, so they have limited flexibility in various scenarios. To solve this problem, this paper proposes an attribute disentanglement network (ADNet) to generate perturbed images using attribute codes from the attribute disentanglement module (ADM), thereby obfuscating arbitrary classifiers while preserving identity recognition. Specifically, the designed ADM can separate attribute-related and attribute-unrelated codes from attribute codes, realizing privacy protection during the transformation of attribute-related code and preserving unrelated information. Moreover, a control factor \(\alpha\) α is used to adjust the degree of transformation, and its value can be adjusted to meet various requirements. Extensive experimental results indicate that ADNet provides controllability for multi-attribute privacy protection while maintaining identity recognition utility. It meets the demands of different protection scenarios and outperforms previous soft-biometric privacy protection strategies.