Kim Sao Nguyen, Ngoc Dung Bui^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.069355 - 10 November 2025

Abstract Reversible data hiding (RDH) enables secret data embedding while preserving complete cover image recovery, making it crucial for applications requiring image integrity. The pixel value ordering (PVO) technique used in multi-stego images provides good image quality but often results in low embedding capability. To address these challenges, this paper proposes a high-capacity RDH scheme based on PVO that generates three stego images from a single cover image. The cover image is partitioned into non-overlapping blocks with pixels sorted in ascending order. Four secret bits are embedded into each block’s maximum pixel value, while three additional More >

Da-Chun Wu^*, Bing-Han Sie

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 535-580, 2025, DOI:10.32604/cmes.2025.067239 - 31 July 2025

Abstract Data hiding methods involve embedding secret messages into cover objects to enable covert communication in a way that is difficult to detect. In data hiding methods based on image interpolation, the image size is reduced and then enlarged through interpolation, followed by the embedding of secret data into the newly generated pixels. A general improving approach for embedding secret messages is proposed. The approach may be regarded a general model for enhancing the data embedding capacity of various existing image interpolation-based data hiding methods. This enhancement is achieved by expanding the range of pixel values… More >

Peizheng Lai¹, Minqing Zhang^1,2,*, Yixin Tang¹, Ya Yue¹, Fuqiang Di^1,2

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2935-2957, 2025, DOI:10.32604/cmc.2025.065887 - 03 July 2025

Abstract Federated Learning (FL) has emerged as a promising distributed machine learning paradigm that enables multi-party collaborative training while eliminating the need for raw data sharing. However, its reliance on a server introduces critical security vulnerabilities: malicious servers can infer private information from received local model updates or deliberately manipulate aggregation results. Consequently, achieving verifiable aggregation without compromising client privacy remains a critical challenge. To address these problem, we propose a reversible data hiding in encrypted domains (RDHED) scheme, which designs joint secret message embedding and extraction mechanism. This approach enables clients to embed secret messages… More >

Yuan-Yu Tsai^1,*, Alfrindo Lin¹, Wen-Ting Jao¹, Yi-Hui Chen^2,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 681-697, 2025, DOI:10.32604/cmc.2025.064370 - 09 June 2025

Abstract With the rapid expansion of multimedia data, protecting digital information has become increasingly critical. Reversible data hiding offers an effective solution by allowing sensitive information to be embedded in multimedia files while enabling full recovery of the original data after extraction. Audio, as a vital medium in communication, entertainment, and information sharing, demands the same level of security as images. However, embedding data in encrypted audio poses unique challenges due to the trade-offs between security, data integrity, and embedding capacity. This paper presents a novel interpolation-based reversible data hiding algorithm for encrypted audio that achieves… More >

Hanan Hardan^1,*, Osama A. Khashan^2,*, Mohammad Alshinwan¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1681-1710, 2025, DOI:10.32604/cmc.2025.061659 - 09 June 2025

Abstract This study introduces an Edge-Based Data Hiding and Extraction Algorithm (EBDHEA) to address the problem of data embedding in images while preserving robust security and high image quality. The algorithm produces three classes of pixels from the pixels in the cover image: edges found by the Canny edge detection method, pixels arising from the expansion of neighboring edge pixels, and pixels that are neither edges nor components of the neighboring edge pixels. The number of Least Significant Bits (LSBs) that are used to hide data depends on these classifications. Furthermore, the lossless compression method, Huffman… More >

Mardhatillah Shevy Ananti¹, Adifa Widyadhani Chanda D’Layla¹, Ntivuguruzwa Jean De La Croix^1,2, Tohari Ahmad^1,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1031-1054, 2025, DOI:10.32604/cmc.2025.061246 - 09 June 2025

Abstract In the evolving landscape of secure communication, steganography has become increasingly vital to secure the transmission of secret data through an insecure public network. Several steganographic algorithms have been proposed using digital images with a common objective of balancing a trade-off between the payload size and the quality of the stego image. In the existing steganographic works, a remarkable distortion of the stego image persists when the payload size is increased, making several existing works impractical to the current world of vast data. This paper introduces FuzzyStego, a novel approach designed to enhance the stego… More >

Lord Amoah^1,2, Jinwei Wang^1,2,3,*, Bernard-Marie Onzo^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1635-1660, 2025, DOI:10.32604/cmes.2025.063992 - 30 May 2025

Abstract Medical image segmentation, i.e., labeling structures of interest in medical images, is crucial for disease diagnosis and treatment in radiology. In reversible data hiding in medical images (RDHMI), segmentation consists of only two regions: the focal and nonfocal regions. The focal region mainly contains information for diagnosis, while the nonfocal region serves as the monochrome background. The current traditional segmentation methods utilized in RDHMI are inaccurate for complex medical images, and manual segmentation is time-consuming, poorly reproducible, and operator-dependent. Implementing state-of-the-art deep learning (DL) models will facilitate key benefits, but the lack of domain-specific labels… More >

Muath AlShaikh^*

Computer Systems Science and Engineering, Vol.49, pp. 435-453, 2025, DOI:10.32604/csse.2025.062432 - 13 May 2025

Abstract Many existing watermarking approaches aim to provide a Robust Reversible Data Hiding (RRDH) method. However, most of these approaches degrade under geometric and non-geometric attacks. This paper presents a novel RRDH approach using Polar Harmonic Fourier Moments (PHFMs) and linear interpolation. The primary objective is to enhance the robustness of the embedded watermark and improve the imperceptibility of the watermarked image. The proposed method leverages the high-fidelity and anti-geometric transformation properties of PHFMs. The image is transformed into the frequency domain of RRDH, after which compensation data is embedded using a two-dimensional RDH scheme. Linear… More >

Zongbao Jiang, Minqing Zhang^*, Weina Dong, Chao Jiang, Fuqiang Di

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1123-1155, 2024, DOI:10.32604/cmc.2024.055120 - 15 October 2024

Abstract With the rapid advancement of cloud computing technology, reversible data hiding algorithms in encrypted images (RDH-EI) have developed into an important field of study concentrated on safeguarding privacy in distributed cloud environments. However, existing algorithms often suffer from low embedding capacities and are inadequate for complex data access scenarios. To address these challenges, this paper proposes a novel reversible data hiding algorithm in encrypted images based on adaptive median edge detection (AMED) and ciphertext-policy attribute-based encryption (CP-ABE). This proposed algorithm enhances the conventional median edge detection (MED) by incorporating dynamic variables to improve pixel prediction… More >

Isaac Asare Boateng^1,2,*, Lord Amoah², Isogun Toluwalase Adewale³

Journal of Information Hiding and Privacy Protection, Vol.6, pp. 1-19, 2024, DOI:10.32604/jihpp.2024.051354 - 24 June 2024

Abstract Contrast enhancement in medical images has been vital since the prevalence of image representations in healthcare. In this research, the PRDHMCE (pairwise reversible data hiding for medical images with contrast enhancement) algorithm is proposed as an automatic contrast enhancement (CE) method for medical images based on region of interest (ROI) and non-region of interest (NROI). The PRDHMCE algorithm strategically enhances the ROI after segmentation using histogram stretching and data embedding. An initial histogram evaluation compares histogram bins with their neighbours to select the bin with the maximum pixel count. The selected bin is set as More >