Multiface Image Compression Encryption Scheme Combining Extraction With STP-CS for Face Database

With the rapid development of the Internet, face recognition technology is widely used, which makes the protection of face database especially important. To protect the recognized faces, a multiface image compression encryption (MFICE) scheme is designed based on the electromagnetic radiation Ktz neuron (ERKN). Since only faces are to be encrypted, they are first extracted. Then the face images are compressed by using semi-tensor product compressed sensing (STP-CS) algorithm, and the compressed images are integrated into a large cube, i.e., a 3-D cube. After that, interface confusion algorithm, 3-D shuffling algorithm, and 3-D diffusion algorithm are sequently performed by using chaotic sequences generated by iteration of ERKN, and finally the ciphertext image cube is obtained. The proposed scheme is evaluated, and it performs well in terms of feasibility and security.

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Publication Details

Journal
IEEE Internet of Things Journal
Published
2025-02-12
DOI
https://doi.org/10.1109/jiot.2025.3541228
Citations
14
Primary Topic
Biometric Identification and Security
Type
article
Field-Weighted Citation Impact
20.79
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article

Multiface Image Compression Encryption Scheme Combining Extraction With STP-CS for Face Database

Jun Mou, Yinghong Cao, Yushu Zhang, Nanrun Zhou et al.
14 citations
IEEE Internet of Things Journal
Biometric Identification and Security
20.79
article

Multiface Image Compression Encryption Scheme Combining Extraction With STP-CS for Face Database

Jun Mou, Yinghong Cao, Yushu Zhang, Nanrun Zhou, Linlin Tan
article en
14 citations

Abstract

With the rapid development of the Internet, face recognition technology is widely used, which makes the protection of face database especially important. To protect the recognized faces, a multiface image compression encryption (MFICE) scheme is designed based on the electromagnetic radiation Ktz neuron (ERKN). Since only faces are to be encrypted, they are first extracted. Then the face images are compressed by using semi-tensor product compressed sensing (STP-CS) algorithm, and the compressed images are integrated into a large cube, i.e., a 3-D cube. After that, interface confusion algorithm, 3-D shuffling algorithm, and 3-D diffusion algorithm are sequently performed by using chaotic sequences generated by iteration of ERKN, and finally the ciphertext image cube is obtained. The proposed scheme is evaluated, and it performs well in terms of feasibility and security.

IEEE Internet of Things JournalVol. 12(12)
Shanghai University of Engineering Science (CN), Jiangxi University of Finance and Economics (CN), Dalian Polytechnic University (CN)
Openalex Percentile: Top 0%
Biometric Identification and Security
20.79
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