A Novel Encryption-Then-Lossy-Compression Scheme of Color Images Using Customized Residual Dense Spatial Network

Nowadays it has still remained as a big challenge to efficiently compress color images in the encrypted domain. In this paper we present a novel deep-learning-based approach to encryption-then-lossy-compression (ETC) of color images by incorporating the domain knowledge of the encrypted image recons...

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Published in:	IEEE transactions on multimedia Vol. 25; pp. 4026 - 4040
Main Authors:	Wang, Chuntao, Zhang, Tianjian, Chen, Hao, Huang, Qiong, Ni, Jiangqun, Zhang, Xinpeng
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Cloud computing Color Color imagery Constraints Cryptography Customization Deep learning Encrypted image compression Encryption Feature extraction Image coding Image compression Image reconstruction residual dense network Sampling spatial attention mechanism super-resolution reconstruction
ISSN:	1520-9210, 1941-0077
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Abstract	Nowadays it has still remained as a big challenge to efficiently compress color images in the encrypted domain. In this paper we present a novel deep-learning-based approach to encryption-then-lossy-compression (ETC) of color images by incorporating the domain knowledge of the encrypted image reconstruction process. In specific, a simple yet effective uniform down-sampling is utilized for lossy compression of images encrypted with a modulo-256 addition, and the task of image reconstruction from an encrypted down-sampled image is then formulated as a problem of constrained super-resolution (SR) reconstruction. A customized residual dense spatial network (RDSN) is proposed to solve the formulated constrained SR task by taking advantage of spatial attention mechanism (SAM), global skip connection (GSC), and uniform down-sampling constraint (UDC) that is specific to an ETC system. Extensive experimental results show that the proposed ETC scheme achieves significant performance improvement compared with other state-of-the-art ETC methods, indicating the feasibility and effectiveness of the proposed deep-learning based ETC scheme.
AbstractList	Nowadays it has still remained as a big challenge to efficiently compress color images in the encrypted domain. In this paper we present a novel deep-learning-based approach to encryption-then-lossy-compression (ETC) of color images by incorporating the domain knowledge of the encrypted image reconstruction process. In specific, a simple yet effective uniform down-sampling is utilized for lossy compression of images encrypted with a modulo-256 addition, and the task of image reconstruction from an encrypted down-sampled image is then formulated as a problem of constrained super-resolution (SR) reconstruction. A customized residual dense spatial network (RDSN) is proposed to solve the formulated constrained SR task by taking advantage of spatial attention mechanism (SAM), global skip connection (GSC), and uniform down-sampling constraint (UDC) that is specific to an ETC system. Extensive experimental results show that the proposed ETC scheme achieves significant performance improvement compared with other state-of-the-art ETC methods, indicating the feasibility and effectiveness of the proposed deep-learning based ETC scheme.
Author	Wang, Chuntao Huang, Qiong Ni, Jiangqun Zhang, Xinpeng Chen, Hao Zhang, Tianjian
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Snippet	Nowadays it has still remained as a big challenge to efficiently compress color images in the encrypted domain. In this paper we present a novel...
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SubjectTerms	Cloud computing Color Color imagery Constraints Cryptography Customization Deep learning Encrypted image compression Encryption Feature extraction Image coding Image compression Image reconstruction residual dense network Sampling spatial attention mechanism super-resolution reconstruction
Title	A Novel Encryption-Then-Lossy-Compression Scheme of Color Images Using Customized Residual Dense Spatial Network
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