Shu-Chuan Chu^1,2, Libin Fu², Jeng-Shyang Pan^2,3, Xingsi Xue⁴, Min Liu^5,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3135-3163, 2025, DOI:10.32604/cmc.2025.062469 - 16 April 2025

Abstract Evolutionary algorithms have been extensively utilized in practical applications. However, manually designed population updating formulas are inherently prone to the subjective influence of the designer. Genetic programming (GP), characterized by its tree-based solution structure, is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world problems. This paper introduces a GP-based framework (GP-EAs) for the autonomous generation of update formulas, aiming to reduce human intervention. Partial modifications to tree-based GP have been instigated, encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the algorithm.… More >

Conghuan Ye^1,*, Shenglong Tan¹, Shi Li¹, Jun Wang¹, Qiankun Zuo¹, Bing Xiong²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2927-2946, 2025, DOI:10.32604/cmc.2025.062051 - 16 April 2025

Abstract With the fast development of multimedia social platforms, content dissemination on social media platforms is becoming more popular. Social image sharing can also raise privacy concerns. Image encryption can protect social images. However, most existing image protection methods cannot be applied to multimedia social platforms because of encryption in the spatial domain. In this work, the authors propose a secure social image-sharing method with watermarking/fingerprinting and encryption. First, the fingerprint code with a hierarchical community structure is designed based on social network analysis. Then, discrete wavelet transform (DWT) from block discrete cosine transform (DCT) directly… More >

Balsam Dhyia Majeed^1,2, Amir Hossein Taherinia^1,*, Hadi Sadoghi Yazdi¹, Ahad Harati¹

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1027-1047, 2025, DOI:10.32604/cmc.2024.059789 - 03 January 2025

Abstract Watermarking is embedding visible or invisible data within media to verify its authenticity or protect copyright. The watermark is embedded in significant spatial or frequency features of the media to make it more resistant to intentional or unintentional modification. Some of these features are important perceptual features according to the human visual system (HVS), which means that the embedded watermark should be imperceptible in these features. Therefore, both the designers of watermarking algorithms and potential attackers must consider these perceptual features when carrying out their actions. The two roles will be considered in this paper… More >

Zhimao Lai^1,2, Saad Arif³, Cong Feng⁴, Guangjun Liao⁵, Chuntao Wang^6,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 73-102, 2025, DOI:10.32604/cmc.2024.059370 - 03 January 2025

Abstract With the rapid advancement of visual generative models such as Generative Adversarial Networks (GANs) and stable Diffusion, the creation of highly realistic Deepfake through automated forgery has significantly progressed. This paper examines the advancements in Deepfake detection and defense technologies, emphasizing the shift from passive detection methods to proactive digital watermarking techniques. Passive detection methods, which involve extracting features from images or videos to identify forgeries, encounter challenges such as poor performance against unknown manipulation techniques and susceptibility to counter-forensic tactics. In contrast, proactive digital watermarking techniques embed specific markers into images or videos, facilitating More >

Satya Narayan Das^1,2,*, Mrutyunjaya Panda^2,*

Intelligent Automation & Soft Computing, Vol.39, No.6, pp. 1073-1100, 2024, DOI:10.32604/iasc.2024.057013 - 30 December 2024

Abstract In today’s world of massive data and interconnected networks, it’s crucial to burgeon a secure and efficient digital watermarking method to protect the copyrights of digital content. Existing research primarily focuses on deep learning-based approaches to improve the quality of watermarked images, but they have some flaws. To overcome this, the deep learning digital image watermarking model with highly secure algorithms is proposed to secure the digital image. Recently, quantum logistic maps, which combine the concept of quantum computing with traditional techniques, have been considered a niche and promising area of research that has attracted… More >

Yucheng Liang^1,2,*, Ke Niu^1,2,*, Yingnan Zhang^1,2, Yifei Meng^1,2, Fangmeng Hu^1,2

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2457-2492, 2024, DOI:10.32604/cmc.2024.056438 - 18 November 2024

Abstract To address the challenges of video copyright protection and ensure the perfect recovery of original video, we propose a dual-domain watermarking scheme for digital video, inspired by Robust Reversible Watermarking (RRW) technology used in digital images. Our approach introduces a parameter optimization strategy that incrementally adjusts scheme parameters through attack simulation fitting, allowing for adaptive tuning of experimental parameters. In this scheme, the low-frequency Polar Harmonic Transform (PHT) moment is utilized as the embedding domain for robust watermarking, enhancing stability against simulation attacks while implementing the parameter optimization strategy. Through extensive attack simulations across various… More >

Yuanjing Luo, Xichen Tan, Zhiping Cai^*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 133-160, 2024, DOI:10.32604/cmc.2024.055150 - 15 October 2024

Abstract In the era of internet proliferation, safeguarding digital media copyright and integrity, especially for images, is imperative. Digital watermarking stands out as a pivotal solution for image security. With the advent of deep learning, watermarking has seen significant advancements. Our review focuses on the innovative deep watermarking approaches that employ neural networks to identify robust embedding spaces, resilient to various attacks. These methods, characterized by a streamlined encoder-decoder architecture, have shown enhanced performance through the incorporation of novel training modules. This article offers an in-depth analysis of deep watermarking’s core technologies, current status, and prospective More >

Wenquan Sun^1,2, Jia Liu^1,2,*, Weina Dong^1,2, Lifeng Chen^1,2, Fuqiang Di^1,2

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4065-4083, 2024, DOI:10.32604/cmc.2024.053115 - 12 September 2024

Abstract As neural radiance fields continue to advance in 3D content representation, the copyright issues surrounding 3D models oriented towards implicit representation become increasingly pressing. In response to this challenge, this paper treats the embedding and extraction of neural radiance field watermarks as inverse problems of image transformations and proposes a scheme for protecting neural radiance field copyrights using invertible neural network watermarking. Leveraging 2D image watermarking technology for 3D scene protection, the scheme embeds watermarks within the training images of neural radiance fields through the forward process in invertible neural networks and extracts them from… More >

Wenquan Sun^1,2, Jia Liu^1,2,*, Lifeng Chen^1,2, Weina Dong^1,2, Fuqiang Di^1,2

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4031-4046, 2024, DOI:10.32604/cmc.2024.052745 - 12 September 2024

Abstract Recent research advances in implicit neural representation have shown that a wide range of video data distributions are achieved by sharing model weights for Neural Representation for Videos (NeRV). While explicit methods exist for accurately embedding ownership or copyright information in video data, the nascent NeRV framework has yet to address this issue comprehensively. In response, this paper introduces MarkINeRV, a scheme designed to embed watermarking information into video frames using an invertible neural network watermarking approach to protect the copyright of NeRV, which models the embedding and extraction of watermarks as a pair of… More >

Lifeng Chen^1,2, Jia Liu^1,2,*, Wenquan Sun^1,2, Weina Dong^1,2, Xiaozhong Pan^1,2

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1235-1250, 2024, DOI:10.32604/cmc.2024.051608 - 18 July 2024

Abstract This paper presents a novel watermarking scheme designed to address the copyright protection challenges encountered with Neural radiation field (NeRF) models. We employ an embedding network to integrate the watermark into the images within the training set. Then, the NeRF model is utilized for 3D modeling. For copyright verification, a secret image is generated by inputting a confidential viewpoint into NeRF. On this basis, design an extraction network to extract embedded watermark images from confidential viewpoints. In the event of suspicion regarding the unauthorized usage of NeRF in a black-box scenario, the verifier can extract More >