Hsiao-Chun Han¹, Der-Chen Huang^1,*, Chin-Ling Chen^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 67-126, 2025, DOI:10.32604/cmes.2025.070944 - 30 October 2025

Abstract This study presents a systematic review of applications of artificial intelligence (abbreviated as AI) and blockchain in supply chain provenance traceability and legal forensics cover five sectors: integrated circuits (abbreviated as ICs), pharmaceuticals, electric vehicles (abbreviated as EVs), drones (abbreviated as UAVs), and robotics—in response to rising trade tensions and geopolitical conflicts, which have heightened concerns over product origin fraud and information security. While previous literature often focuses on single-industry contexts or isolated technologies, this review comprehensively surveys these sectors and categorizes 116 peer-reviewed studies by application domain, technical architecture, and functional objective. Special attention More >

Idarous Saleh Said¹, Gilbert Gilibrays Ocen^1,*, Mwase Ali², Alunyu Andrew Egwar¹

Journal of Cyber Security, Vol.7, pp. 359-375, 2025, DOI:10.32604/jcs.2025.066979 - 25 September 2025

Abstract The growing reliance on digital evidence in judicial proceedings has heightened the need for structured, secure, and legally sound frameworks for its collection, preservation, storage, and presentation. In Zanzibar, however, the integration of digital evidence into the court system remains hindered by the absence of standardized procedures and digital infrastructure, undermining the integrity and admissibility of such evidence. This study addresses these challenges by developing a comprehensive Digital Evidence Lifecycle Management Framework (DELM-C) tailored to the operational and legal context of the Zanzibar High Court. The proposed framework aims to streamline digital evidence handling, enhance… 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 >

Salam Al-E’mari¹, Yousef Sanjalawe^2,*, Budoor Allehyani³, Ghader Kurdi⁴, Sharif Makhadmeh², Ameera Jaradat⁵, Duaa Hijazi⁶

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3289-3320, 2025, DOI:10.32604/cmc.2025.067950 - 23 September 2025

Abstract Electric Vehicle Charging Systems (EVCS) are increasingly vulnerable to cybersecurity threats as they integrate deeply into smart grids and Internet of Things (IoT) environments, raising significant security challenges. Most existing research primarily emphasizes network-level anomaly detection, leaving critical vulnerabilities at the host level underexplored. This study introduces a novel forensic analysis framework leveraging host-level data, including system logs, kernel events, and Hardware Performance Counters (HPC), to detect and analyze sophisticated cyberattacks such as cryptojacking, Denial-of-Service (DoS), and reconnaissance activities targeting EVCS. Using comprehensive forensic analysis and machine learning models, the proposed framework significantly outperforms existing More >

Hazem Farah¹, Akram Bennour^1,*, Hama Soltani¹, Mouaaz Nahas², Rashiq Rafiq Marie³, Mohammed Al-Sarem^3,4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3335-3348, 2025, DOI:10.32604/cmc.2025.067226 - 23 September 2025

Abstract This study presents an advanced method for post-mortem person identification using the segmentation of skeletal structures from chest X-ray images. The proposed approach employs the Attention U-Net architecture, enhanced with gated attention mechanisms, to refine segmentation by emphasizing spatially relevant anatomical features while suppressing irrelevant details. By isolating skeletal structures which remain stable over time compared to soft tissues, this method leverages bones as reliable biometric markers for identity verification. The model integrates custom-designed encoder and decoder blocks with attention gates, achieving high segmentation precision. To evaluate the impact of architectural choices, we conducted an… 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 >

Saurabh Agarwal¹, Deepak Sharma², Nancy Girdhar³, Cheonshik Kim⁴, Ki-Hyun Jung^5,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4195-4221, 2025, DOI:10.32604/cmc.2025.066202 - 30 July 2025

Abstract In today’s digital era, the rapid evolution of image editing technologies has brought about a significant simplification of image manipulation. Unfortunately, this progress has also given rise to the misuse of manipulated images across various domains. One of the pressing challenges stemming from this advancement is the increasing difficulty in discerning between unaltered and manipulated images. This paper offers a comprehensive survey of existing methodologies for detecting image tampering, shedding light on the diverse approaches employed in the field of contemporary image forensics. The methods used to identify image forgery can be broadly classified into… More >

Dong-Hyuk Shin¹, Jae-Jun Ha¹, Ieck-Chae Euom^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3795-3838, 2025, DOI:10.32604/cmes.2025.066727 - 30 June 2025

Abstract The accelerated global adoption of electric vehicles (EVs) is driving significant expansion and increasing complexity within the EV charging infrastructure, consequently presenting novel and pressing cybersecurity challenges. While considerable effort has focused on preventative cybersecurity measures, a critical deficiency persists in structured methodologies for digital forensic analysis following security incidents, a gap exacerbated by system heterogeneity, distributed digital evidence, and inconsistent logging practices which hinder effective incident reconstruction and attribution. This paper addresses this critical need by proposing a novel, data-driven forensic framework tailored to the EV charging infrastructure, focusing on the systematic identification, classification,… More >

Ananya Jha¹, Amaresh Jha^2,*

Journal of Cyber Security, Vol.7, pp. 109-134, 2025, DOI:10.32604/jcs.2025.064429 - 30 May 2025

Abstract This paper explores the methodologies employed in the study of mobile and social media phishing, aiming to enhance the understanding of these evolving threats and develop robust countermeasures. By synthesizing existing research, we identify key approaches, including surveys, controlled experiments, data mining, and machine learning, to gather and analyze data on phishing tactics. These methods enable us to uncover patterns in attacker behavior, pinpoint vulnerabilities in mobile and social platforms, and evaluate the effectiveness of current detection and prevention strategies. Our findings highlight the growing sophistication of phishing techniques, such as social engineering and deceptive More >

Jim Wong^1,2, Zhaoxiang Zang^3,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3451-3469, 2025, DOI:10.32604/cmc.2025.061006 - 16 April 2025

Abstract In the field of image forensics, image tampering detection is a critical and challenging task. Traditional methods based on manually designed feature extraction typically focus on a specific type of tampering operation, which limits their effectiveness in complex scenarios involving multiple forms of tampering. Although deep learning-based methods offer the advantage of automatic feature learning, current approaches still require further improvements in terms of detection accuracy and computational efficiency. To address these challenges, this study applies the U-Net 3+ model to image tampering detection and proposes a hybrid framework, referred to as DDT-Net (Deep Detail… More >