Ahmed El Maloufy, Mohamed Amine Tahiri, Ahmed Bencherqui, Hicham Karmouni, Mhamed Sayyouri^*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5789-5822, 2025, DOI:10.32604/cmc.2025.068885 - 23 October 2025

Abstract In the current digital context, safeguarding copyright is a major issue, particularly for architectural drawings produced by students. These works are frequently the result of innovative academic thinking combining creativity and technical precision. They are particularly vulnerable to the risk of illegal reproduction when disseminated in digital format. This research suggests, for the first time, an innovative approach to copyright protection by embedding a double digital watermark to address this challenge. The solution relies on a synergistic fusion of several sophisticated methods: Krawtchouk Optimized Octonion Moments (OKOM), Quaternion Singular Value Decomposition (QSVD), and Discrete Waveform… More >

Jialun Lin¹, Qiong Chen^1,2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1549-1561, 2024, DOI:10.32604/cmes.2023.029631 - 17 November 2023

Abstract Watermarks can provide reliable and secure copyright protection for optical coherence tomography (OCT) fundus images. The effective image segmentation is helpful for promoting OCT image watermarking. However, OCT images have a large amount of low-quality data, which seriously affects the performance of segmentation methods. Therefore, this paper proposes an effective segmentation method for OCT fundus image watermarking using a rough convolutional neural network (RCNN). First, the rough-set-based feature discretization module is designed to preprocess the input data. Second, a dual attention mechanism for feature channels and spatial regions in the CNN is added to enable… More >

Wenxing Zhang¹, Jingbing Li^1,2,*, Uzair Aslam Bhatti^1,2, Jing Liu³, Junhua Zheng¹, Yen-Wei Chen⁴

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 565-586, 2023, DOI:10.32604/cmc.2023.036317 - 06 February 2023

Abstract The field of medical images has been rapidly evolving since the advent of the digital medical information era. However, medical data is susceptible to leaks and hacks during transmission. This paper proposed a robust multi-watermarking algorithm for medical images based on GoogLeNet transfer learning to protect the privacy of patient data during transmission and storage, as well as to increase the resistance to geometric attacks and the capacity of embedded watermarks of watermarking algorithms. First, a pre-trained GoogLeNet network is used in this paper, based on which the parameters of several previous layers of the… More >