Ahmed Abdel-Wahab¹, Mohammad Alkhatib^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-34, 2026, DOI:10.32604/cmc.2025.070010 - 09 December 2025

Abstract Deepfake is a sort of fake media made by advanced AI methods like Generative Adversarial Networks (GANs). Deepfake technology has many useful uses in education and entertainment, but it also raises a lot of ethical, social, and security issues, such as identity theft, the dissemination of false information, and privacy violations. This study seeks to provide a comprehensive analysis of several methods for identifying and circumventing Deepfakes, with a particular focus on image-based Deepfakes. There are three main types of detection methods: classical, machine learning (ML) and deep learning (DL)-based, and hybrid methods. There are… More >

Hamza Murad Khan¹, Shakila Basheer², Mohammad Tabrez Quasim³, Raja`a Al-Naimi⁴, Vijaykumar Varadarajan⁵, Anwar Khan^1,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.069327 - 10 November 2025

Abstract With the increasing growth of online news, fake electronic news detection has become one of the most important paradigms of modern research. Traditional electronic news detection techniques are generally based on contextual understanding, sequential dependencies, and/or data imbalance. This makes distinction between genuine and fabricated news a challenging task. To address this problem, we propose a novel hybrid architecture, T5-SA-LSTM, which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attention-enhanced (SA) Long Short-Term Memory (LSTM). The LSTM is trained using the Adam optimizer, which provides faster and more stable convergence compared… More >

Pengpeng Yang^1,2,*, Chen Zhou¹, Dasara Shullani², Lanxi Liu¹, Daniele Baracchi²

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2487-2526, 2025, DOI:10.32604/cmc.2025.069129 - 23 September 2025

Abstract Images and videos play an increasingly vital role in daily life and are widely utilized as key evidentiary sources in judicial investigations and forensic analysis. Simultaneously, advancements in image and video processing technologies have facilitated the widespread availability of powerful editing tools, such as Deepfakes, enabling anyone to easily create manipulated or fake visual content, which poses an enormous threat to social security and public trust. To verify the authenticity and integrity of images and videos, numerous approaches have been proposed, which are primarily based on content analysis and their effectiveness is susceptible to interference… More >

Soufiane Khedairia¹, Akram Bennour^2,*, Mouaaz Nahas³, Aida Chefrour¹, Rashiq Rafiq Marie⁴, Mohammed Al-Sarem⁵

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1267-1285, 2025, DOI:10.32604/cmc.2025.066601 - 29 August 2025

Abstract These days, social media has grown to be an integral part of people’s lives. However, it involves the possibility of exposure to “fake news,” which may contain information that is intentionally or inaccurately false to promote particular political or economic interests. The main objective of this work is to use the co-attention mechanism in a Combined Graph neural network model (CMCG) to capture the relationship between user profile features and user preferences in order to detect fake news and examine the influence of various social media features on fake news detection. The proposed approach includes… More >

Muhammad Javed¹, Zhaohui Zhang^1,*, Fida Hussain Dahri², Asif Ali Laghari^3,*, Martin Krajčík⁴, Ahmad Almadhor⁵

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1457-1493, 2025, DOI:10.32604/cmc.2025.062954 - 29 August 2025

Abstract Recent advances in artificial intelligence and the availability of large-scale benchmarks have made deepfake video generation and manipulation easier. Therefore, developing reliable and robust deepfake video detection mechanisms is paramount. This research introduces a novel real-time deepfake video detection framework by analyzing gaze and blink patterns, addressing the spatial-temporal challenges unique to gaze and blink anomalies using the TimeSformer and hybrid Transformer-CNN models. The TimeSformer architecture leverages spatial-temporal attention mechanisms to capture fine-grained blinking intervals and gaze direction anomalies. Compared to state-of-the-art traditional convolutional models like MesoNet and EfficientNet, which primarily focus on global facial… More >

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 >