ML-FAS: Multi-Level Face Anonymization Scheme and Its Application to E-Commerce Systems

With the proliferation of electronic commerce, the facial data used for identity authentication and mobile payment are potentially subject to data analytics and mining attacks by third-party platforms, which has raised public privacy concerns. To tackle the issue, a novel Multi-Level Face Anonymizat...

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Bibliographic Details
Published inIEEE transactions on consumer electronics Vol. 70; no. 3; pp. 5090 - 5100
Main Authors Jiang, Donghua, Ahmad, Jawad, Suo, Zhufeng, Alsulami, Mashael M., Ghadi, Yazeed Yasin, Boulila, Wadii
Format Journal Article
LanguageEnglish
Published New York IEEE 01.08.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Chaos theory
chaotic system
Cryptography
data anonymization
Data privacy
Deep learning
Electronic commerce
Encryption
Faces
Image reconstruction
Information filtering
Information integrity
known-plaintext attack
Mobile commerce
physical unclonable function
Privacy
Superlattices
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Summary:With the proliferation of electronic commerce, the facial data used for identity authentication and mobile payment are potentially subject to data analytics and mining attacks by third-party platforms, which has raised public privacy concerns. To tackle the issue, a novel Multi-Level Face Anonymization Scheme (ML-FAS) based on deep learning technology is proposed in this paper. Firstly, a 4-D chaotic system is employed to construct different levels of keys with initial parameters securely distributed and managed using the Semiconductor SuperLattice Physical Unclonable Function (SSL-PUF). Secondly, under the guidance of the known prior distribution and adversarial training strategy, a noise-like cipher image is generated by the encryption network to withstand the known-plaintext attacks. Besides, different levels of recipients can leverage the identical decryption network to reconstruct the facial images with varying visual content. Compared with the existing manually designed anonymization schemes, the ML-FAS possesses several significant merits. Finally, extensive simulation experiments verified the effectiveness of the proposed scheme, including its security and robustness. The code is available at https://github.com/DonghuaJiang/MLFAS .
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ISSN:0098-3063
1558-4127
DOI:10.1109/TCE.2024.3411102