Juhwan Kim, Baehoon Son, Jihyeon Yu, Joobeom Yun^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3371-3393, 2024, DOI:10.32604/cmc.2024.057234 - 18 November 2024

Abstract Digital forensics aims to uncover evidence of cybercrimes within compromised systems. These cybercrimes are often perpetrated through the deployment of malware, which inevitably leaves discernible traces within the compromised systems. Forensic analysts are tasked with extracting and subsequently analyzing data, termed as artifacts, from these systems to gather evidence. Therefore, forensic analysts must sift through extensive datasets to isolate pertinent evidence. However, manually identifying suspicious traces among numerous artifacts is time-consuming and labor-intensive. Previous studies addressed such inefficiencies by integrating artificial intelligence (AI) technologies into digital forensics. Despite the efforts in previous studies, artifacts were… More >

Huan Wang¹, Hong Wang¹, Zhongyuan Jiang^2,*, Qing Qian¹, Yong Long¹

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4603-4620, 2024, DOI:10.32604/cmc.2024.053740 - 12 September 2024

Abstract Copy-Move Forgery Detection (CMFD) is a technique that is designed to identify image tampering and locate suspicious areas. However, the practicality of the CMFD is impeded by the scarcity of datasets, inadequate quality and quantity, and a narrow range of applicable tasks. These limitations significantly restrict the capacity and applicability of CMFD. To overcome the limitations of existing methods, a novel solution called IMTNet is proposed for CMFD by employing a feature decoupling approach. Firstly, this study formulates the objective task and network relationship as an optimization problem using transfer learning. Furthermore, it thoroughly discusses… More >

Jianfeng Lu^1,2, Caijin Li¹, Xiangye Huang¹, Chen Cui³, Mahmoud Emam^1,2,4,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3047-3065, 2024, DOI:10.32604/cmc.2024.053680 - 15 August 2024

Abstract The widespread availability of digital multimedia data has led to a new challenge in digital forensics. Traditional source camera identification algorithms usually rely on various traces in the capturing process. However, these traces have become increasingly difficult to extract due to wide availability of various image processing algorithms. Convolutional Neural Networks (CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera devices. However, their performances is not ideal in case of distinguishing between individual devices of the same model, because cameras of the same model typically use the same… More >

Aiman¹, Muhammad Arshad^1,*, Bilal Khan¹, Sadique Ahmad^2,*, Muhammad Asim^2,3

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3333-3353, 2024, DOI:10.32604/cmc.2024.049254 - 15 May 2024

Abstract Author Profiling (AP) is a subsection of digital forensics that focuses on the detection of the author’s personal information, such as age, gender, occupation, and education, based on various linguistic features, e.g., stylistic, semantic, and syntactic. The importance of AP lies in various fields, including forensics, security, medicine, and marketing. In previous studies, many works have been done using different languages, e.g., English, Arabic, French, etc. However, the research on Roman Urdu is not up to the mark. Hence, this study focuses on detecting the author’s age and gender based on Roman Urdu text messages.… More >

Yasmine M. Ibrahim^1,2, Reem Essameldin³, Saad M. Darwish^1,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 243-262, 2024, DOI:10.32604/cmc.2024.047840 - 25 April 2024

Abstract Detecting hate speech automatically in social media forensics has emerged as a highly challenging task due to the complex nature of language used in such platforms. Currently, several methods exist for classifying hate speech, but they still suffer from ambiguity when differentiating between hateful and offensive content and they also lack accuracy. The work suggested in this paper uses a combination of the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) to adjust the weights of two Multi-Layer Perceptron (MLPs) for neutrosophic sets classification. During the training process of the MLP, the WOA is More >

Guangjun Liang^1,2,3, Jianfang Xin^4,*, Qun Wang^1,2, Xueli Ni^1,2,3, Xiangmin Guo^1,2,3, Pu Chen¹

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 799-825, 2023, DOI:10.32604/cmc.2023.038403 - 31 October 2023

Abstract As a subversive concept, the metaverse has recently attracted widespread attention around the world and has set off a wave of enthusiasm in academic, industrial, and investment circles. However, while the metaverse brings unprecedented opportunities for transformation to human society, it also contains related risks. Metaverse is a digital living space with information infrastructure, interoperability system, content production system, and value settlement system as the underlying structure in which the inner core is to connect real residents through applications and identities. Through social incentives and governance rules, the metaverse reflects the digital migration of human… More >

Asif Rahim¹, Yanru Zhong², Tariq Ahmad^3,*, Sadique Ahmad^4,*, Mohammed A. ElAffendi⁴

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1947-1976, 2023, DOI:10.32604/cmc.2023.039340 - 30 August 2023

Abstract Authorship verification is a crucial task in digital forensic investigations, where it is often necessary to determine whether a specific individual wrote a particular piece of text. Convolutional Neural Networks (CNNs) have shown promise in solving this problem, but their performance highly depends on the choice of hyperparameters. In this paper, we explore the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification. We conduct experiments using a Hyper Tuned CNN model with three popular optimization algorithms: Adaptive Moment Estimation (ADAM), Stochastic Gradient Descent (SGD), and Root Mean Squared Propagation (RMSPROP).… More >

Xiaojie Zhao¹, Xiankui Meng¹, Ruyong Ren², Shaozhang Niu^2,*, Zhenguang Gao³

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1765-1781, 2023, DOI:10.32604/cmc.2023.029552 - 06 February 2023

Abstract Detecting double Joint Photographic Experts Group (JPEG) compression for color images is vital in the field of image forensics. In previous researches, there have been various approaches to detecting double JPEG compression with different quantization matrices. However, the detection of double JPEG color images with the same quantization matrix is still a challenging task. An effective detection approach to extract features is proposed in this paper by combining traditional analysis with Convolutional Neural Networks (CNN). On the one hand, the number of nonzero pixels and the sum of pixel values of color space conversion error… More >

Waleed Halboob^1,*, Jalal Almuhtadi^1,2

Computer Systems Science and Engineering, Vol.45, No.2, pp. 2071-2092, 2023, DOI:10.32604/csse.2023.024110 - 03 November 2022

Abstract Privacy preservation (PP) in Digital forensics (DF) is a conflicted and non-trivial issue. Existing solutions use the searchable encryption concept and, as a result, are not efficient and support only a keyword search. Moreover, the collected forensic data cannot be analyzed using existing well-known digital tools. This research paper first investigates the lawful requirements for PP in DF based on the organization for economic co-operation and development OECB) privacy guidelines. To have an efficient investigation process and meet the increased volume of data, the presented framework is designed based on the selective imaging concept and… More >

Sultan Alkaabi^1,*, Salman Yussof¹, Sameera Al-Mulla²

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2499-2516, 2023, DOI:10.32604/cmc.2023.029914 - 31 October 2022

Abstract Age estimation using forensics odontology is an important process in identifying victims in criminal or mass disaster cases. Traditionally, this process is done manually by human expert. However, the speed and accuracy may vary depending on the expertise level of the human expert and other human factors such as level of fatigue and attentiveness. To improve the recognition speed and consistency, researchers have proposed automated age estimation using deep learning techniques such as Convolutional Neural Network (CNN). CNN requires many training images to obtain high percentage of recognition accuracy. Unfortunately, it is very difficult to… More >