Yong Liu^1,2, Tianning Sun^3,*, Daofu Gong^1,4, Li Di⁵, Xu Zhao¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1161-1184, 2025, DOI:10.32604/cmc.2025.062330 - 29 August 2025

Abstract To address the high-quality forged videos, traditional approaches typically have low recognition accuracy and tend to be easily misclassified. This paper tries to address the challenge of detecting high-quality deepfake videos by promoting the accuracy of Artificial Intelligence Generated Content (AIGC) video authenticity detection with a multimodal information fusion approach. First, a high-quality multimodal video dataset is collected and normalized, including resolution correction and frame rate unification. Next, feature extraction techniques are employed to draw out features from visual, audio, and text modalities. Subsequently, these features are fused into a multilayer perceptron and attention mechanisms-based More >

Akanbi Bolakale AbdulQudus¹, Oluwatosin Ahmed Amodu^2,3,*, Umar Ali Bukar⁴, Raja Azlina Raja Mahmood², Anies Faziehan Zakaria⁵, Saki-Ogah Queen⁶, Zurina Mohd Hanapi²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 153-236, 2025, DOI:10.32604/cmc.2025.061427 - 09 June 2025

Abstract This paper provides a comprehensive bibliometric exposition on deepfake research, exploring the intersection of artificial intelligence and deepfakes as well as international collaborations, prominent researchers, organizations, institutions, publications, and key themes. We performed a search on the Web of Science (WoS) database, focusing on Artificial Intelligence and Deepfakes, and filtered the results across 21 research areas, yielding 1412 articles. Using VOSviewer visualization tool, we analyzed this WoS data through keyword co-occurrence graphs, emphasizing on four prominent research themes. Compared with existing bibliometric papers on deepfakes, this paper proceeds to identify and discuss some of the… More >

Priyadharsini Selvaraj^1,*, Senthil Kumar Jagatheesaperumal², Karthiga Marimuthu¹, Oviya Saravanan¹, Bader Fahad Alkhamees³, Mohammad Mehedi Hassan^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1575-1594, 2025, DOI:10.32604/cmes.2025.064138 - 30 May 2025

Abstract With expeditious advancements in AI-driven facial manipulation techniques, particularly deepfake technology, there is growing concern over its potential misuse. Deepfakes pose a significant threat to society, particularly by infringing on individuals’ privacy. Amid significant endeavors to fabricate systems for identifying deepfake fabrications, existing methodologies often face hurdles in adjusting to innovative forgery techniques and demonstrate increased vulnerability to image and video clarity variations, thereby hindering their broad applicability to images and videos produced by unfamiliar technologies. In this manuscript, we endorse resilient training tactics to amplify generalization capabilities. In adversarial training, models are trained using More >

Qin Wang¹, Xiaofeng Wang^2,*, Jianghua Li², Ruidong Han², Zinian Liu¹, Mingtao Guo³

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4607-4621, 2025, DOI:10.32604/cmc.2025.063141 - 19 May 2025

Abstract The majority of current deepfake detection methods are constrained to identifying one or two specific types of counterfeit images, which limits their ability to keep pace with the rapid advancements in deepfake technology. Therefore, in this study, we propose a novel algorithm, Stereo Mixture Density Network (SMNDNet), which can detect multiple types of deepfake face manipulations using a single network framework. SMNDNet is an end-to-end CNN-based network specially designed for detecting various manipulation types of deepfake face images. First, we design a Subtle Distinguishable Feature Enhancement Module to emphasize the differentiation between authentic and forged… More >

Milinda Priyankara Bandara Gamawelagedara¹, Mian Usman Sattar¹, Raza Hasan^2,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4061-4084, 2025, DOI:10.32604/cmc.2025.062826 - 19 May 2025

Abstract Fuel station drive-offs, wherein the drivers simply drive off without paying, are a major issue in the UK (United Kingdom) due to rising fuel costs and financial hardships. The phenomenon has increased greatly over the last few years, with reports indicating a substantial increase in such events in the major cities. Traditional prevention measures such as Avutec and Driveoffalert rely primarily on expensive infrastructure and blacklisted databases. Such systems typically involve costly camera installation and maintenance and are consequently out of the budget of small fuel stations. These conventional approaches also fall short regarding real-time… More >

Xinyi Wang^*, Wanru Song, Chuanyan Hao, Feng Liu

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3351-3368, 2025, DOI:10.32604/cmc.2025.062922 - 16 April 2025

Abstract As Deepfake technology continues to evolve, the distinction between real and fake content becomes increasingly blurred. Most existing Deepfake video detection methods rely on single-frame facial image features, which limits their ability to capture temporal differences between frames. Current methods also exhibit limited generalization capabilities, struggling to detect content generated by unknown forgery algorithms. Moreover, the diversity and complexity of forgery techniques introduced by Artificial Intelligence Generated Content (AIGC) present significant challenges for traditional detection frameworks, which must balance high detection accuracy with robust performance. To address these challenges, we propose a novel Deepfake detection… More >

Jianxiang Cao¹, Jinyang Wu¹, Wenqian Shang^1,*, Chunhua Wang¹, Kang Song¹, Tong Yi^2,*, Jiajun Cai¹, Haibin Zhu³

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2659-2675, 2025, DOI:10.32604/cmc.2025.060025 - 16 April 2025

Abstract With the rapid growth of social media, the spread of fake news has become a growing problem, misleading the public and causing significant harm. As social media content is often composed of both images and text, the use of multimodal approaches for fake news detection has gained significant attention. To solve the problems existing in previous multi-modal fake news detection algorithms, such as insufficient feature extraction and insufficient use of semantic relations between modes, this paper proposes the MFFFND-Co (Multimodal Feature Fusion Fake News Detection with Co-Attention Block) model. First, the model deeply explores the More >

Yuanqing Li¹, Mengyao Dai¹, Sanfeng Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 309-333, 2025, DOI:10.32604/cmc.2025.060455 - 26 March 2025

Abstract Social media has significantly accelerated the rapid dissemination of information, but it also boosts propagation of fake news, posing serious challenges to public awareness and social stability. In real-world contexts, the volume of trustable information far exceeds that of rumors, resulting in a class imbalance that leads models to prioritize the majority class during training. This focus diminishes the model’s ability to recognize minority class samples. Furthermore, models may experience overfitting when encountering these minority samples, further compromising their generalization capabilities. Unlike node-level classification tasks, fake news detection in social networks operates on graph-level samples,… More >

Oncology Research Editorial Office

Oncology Research, Vol.33, No.4, pp. 991-991, 2025, DOI:10.32604/or.2024.056914 - 19 March 2025

Abstract This article has no abstract. 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 >