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- W4385654023 abstract "In the era of cloud computing, many people pay attention to privacy protection issues while transferring data to the cloud, leading to the emergence of Reversible Data Hiding in Encrypted Images (RDHEI). The main difficulty in RDHEI is how to fully use the redundant room in the image to improve the Embedding Rate (ER). To solve this problem, an efficient RDHEI method based on Multi-Granularity Adaptive Classification (MGAC) mechanism is proposed in this paper. In image encryption, a block-based image encryption algorithm based on Cellular Neural Networks (CNN) hyper-chaotic system is utilized, including confusion and diffusion. The room reservation part uses the MGAC mechanism based on the block’s global correlation and local correlation. Specifically, each block is classified according to the quantitative characteristics of the same bit plane in this block. Afterward, different room reservation strategies are applied to different types of blocks adaptively. The multi-granularity is reflected in each block’s rough reservation and refined reservation operations. The image decryption part realizes the separability of data extraction and image recovery. Extensive experimental results show that the average ER of the proposed method on the datasets BOSSbase, BOWS-2, and UCID is about 0.16bpp higher than that of the baseline method on average. Meanwhile, the image security performance of the proposed method has also been improved." @default.
- W4385654023 created "2023-08-09" @default.
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- W4385654023 date "2023-01-01" @default.
- W4385654023 modified "2023-09-23" @default.
- W4385654023 title "Reversible Data Hiding in Encrypted Images Based on a Multi-Granularity Adaptive Classification Mechanism" @default.
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- W4385654023 doi "https://doi.org/10.1007/978-3-031-40286-9_17" @default.
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