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

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 society. This article will conduct… 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

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). The model is trained and… More >

Xingyu Chen^*

Journal of Information Hiding and Privacy Protection, Vol.4, No.2, pp. 103-113, 2022, DOI:10.32604/jihpp.2022.039766

Abstract With the popularization of high-performance electronic imaging equipment and the wide application of digital image editing software, the threshold of digital image editing becomes lower and lower. This makes it easy to trick the human visual system with professionally altered images. These tampered images have brought serious threats to many fields, including personal privacy, news communication, judicial evidence collection, information security and so on. Therefore, the security and reliability of digital information has been increasingly concerned by the international community. In this paper, digital image tamper detection methods are classified according to the clues that they rely on, detection methods… 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

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 are provided with 12-dimensional features… 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

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 advanced encryption standard (AES). The… 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

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 get large number of samples… More >

Anjani Kumar Rai^*, Subodh Srivastava

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1489-1528, 2023, DOI:10.32604/cmes.2022.020920

Abstract Image forging is the alteration of a digital image to conceal some of the necessary or helpful information. It cannot be easy to distinguish the modified region from the original image in some circumstances. The demand for authenticity and the integrity of the image drive the detection of a fabricated image. There have been cases of ownership infringements or fraudulent actions by counterfeiting multimedia files, including re-sampling or copy-moving. This work presents a high-level view of the forensics of digital images and their possible detection approaches. This work presents a thorough analysis of digital image forgery detection techniques with their… More >

Liang Xiu-jian^1,2,*, Sun He²

Journal of Cyber Security, Vol.4, No.2, pp. 119-133, 2022, DOI:10.32604/jcs.2022.032915

Abstract Increasingly advanced image processing technology has made digital image editing easier and easier. With image processing software at one’s fingertips, one can easily alter the content of an image, and the altered image is so realistic that it is illegible to the naked eye. These tampered images have posed a serious threat to personal privacy, social order, and national security. Therefore, detecting and locating tampered areas in images has important practical significance, and has become an important research topic in the field of multimedia information security. In recent years, deep learning technology has been widely used in image tampering localization,… More >

Haitao Zhang^*

Journal of Information Hiding and Privacy Protection, Vol.4, No.1, pp. 41-51, 2022, DOI:10.32604/jihpp.2022.031707

Abstract Deep learning related technologies, especially generative adversarial network, are widely used in the fields of face image tampering and forgery. Forensics researchers have proposed a variety of passive forensic and related anti-forensic methods for image tampering and forgery, especially face images, but there is still a lack of overview of anti-forensic methods at this stage. Therefore, this paper will systematically discuss the anti-forensic methods for face image tampering and forgery. Firstly, this paper expounds the relevant background, including the relevant tampering and forgery methods and forensic schemes of face images. The former mainly includes four aspects: conventional processing, fake face… More >

C. D. Prem Kumar^1,*, S. Saravana Sundaram²

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 881-899, 2023, DOI:10.32604/iasc.2023.025766

Abstract The extensive availability of advanced digital image technologies and image editing tools has simplified the way of manipulating the image content. An effective technique for tampering the identification is the copy-move forgery. Conventional image processing techniques generally search for the patterns linked to the fake content and restrict the usage in massive data classification. Contrastingly, deep learning (DL) models have demonstrated significant performance over the other statistical techniques. With this motivation, this paper presents an Optimal Deep Transfer Learning based Copy Move Forgery Detection (ODTL-CMFD) technique. The presented ODTL-CMFD technique aims to derive a DL model for the classification of… More >