Shufan Peng, Manchun Cai^*, Tianliang Lu, Xiaowen Liu

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4025-4045, 2023, DOI:10.32604/cmc.2023.036688 - 31 March 2023

Abstract Face forgery detection is drawing ever-increasing attention in the academic community owing to security concerns. Despite the considerable progress in existing methods, we note that: Previous works overlooked fine-grain forgery cues with high transferability. Such cues positively impact the model’s accuracy and generalizability. Moreover, single-modality often causes overfitting of the model, and Red-Green-Blue (RGB) modal-only is not conducive to extracting the more detailed forgery traces. We propose a novel framework for fine-grain forgery cues mining with fusion modality to cope with these issues. First, we propose two functional modules to reveal and locate the deeper… More >

Hüseyin Bilal Macit¹, Arif Koyun^2,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1603-1619, 2023, DOI:10.32604/cmc.2023.036216 - 06 February 2023

Abstract Recently, digital images have become the most used data, thanks to high internet speed and high resolution, cheap and easily accessible digital cameras. We generate, transmit and store millions of images every second. Most of these images are insignificant images containing only personal information. However, in many fields such as banking, finance, public institutions, and educational institutions, the images of many valuable objects like ID cards, photographs, credit cards, and transaction receipts are stored and transmitted to the digital environment. These images are very significant and must be secured. A valuable image can be maliciously… More >

R. Dhanya^1,*, R. Kalaiselvi²

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1577-1593, 2023, DOI:10.32604/csse.2023.031319 - 03 November 2022

Abstract A severe problem in modern information systems is Digital media tampering along with fake information. Even though there is an enhancement in image development, image forgery, either by the photographer or via image manipulations, is also done in parallel. Numerous researches have been concentrated on how to identify such manipulated media or information manually along with automatically; thus conquering the complicated forgery methodologies with effortlessly obtainable technologically enhanced instruments. However, high complexity affects the developed methods. Presently, it is complicated to resolve the issue of the speed-accuracy trade-off. For tackling these challenges, this article put… 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 - 20 September 2022

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 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 - 06 June 2022

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… 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 - 04 July 2022

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… More >

Vinay Kumar^1,*, Vineet Kansal², Manish Gaur²

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1347-1364, 2022, DOI:10.32604/cmc.2022.023545 - 18 May 2022

Abstract With the growth of digital media data manipulation in today’s era due to the availability of readily handy tampering software, the authenticity of records is at high risk, especially in video. There is a dire need to detect such problem and do the necessary actions. In this work, we propose an approach to detect the interframe video forgery utilizing the deep features obtained from the parallel deep neural network model and thorough analytical computations. The proposed approach only uses the deep features extracted from the CNN model and then applies the conventional mathematical approach to… More >

Amira Baumy¹, Abeer D. Algarni^2,*, Mahmoud Abdalla³, Walid El-Shafai^4,5, Fathi E. Abd El-Samie^3,4, Naglaa F. Soliman^2,3

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3257-3276, 2022, DOI:10.32604/cmc.2022.021047 - 07 December 2021

Abstract This paper is concerned with a vital topic in image processing: color image forgery detection. The development of computing capabilities has led to a breakthrough in hacking and forgery attacks on signal, image, and data communicated over networks. Hence, there is an urgent need for developing efficient image forgery detection algorithms. Two main types of forgery are considered in this paper: splicing and copy-move. Splicing is performed by inserting a part of an image into another image. On the other hand, copy-move forgery is performed by copying a part of the image into another position… More >

Maryam Munawar, Iram Noreen^*

Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 927-937, 2021, DOI:10.32604/iasc.2021.018854 - 01 July 2021

Abstract Video and image data is the most important and widely used format of communication today. It is used as evidence and authenticated proof in different domains such as law enforcement, forensic studies, journalism, and others. With the increase of video applications and data, the problem of forgery in video and images has also originated. Although a lot of work has been done on image forgery, video forensic is still a challenging area. Videos are manipulated in many ways. Frame insertion, deletion, and frame duplication are a few of the major challenges. Moreover, in the perspective… More >

Sajid Habib Gill¹, Noor Ahmed Sheikh¹, Samina Rajpar¹, Zain ul Abidin², N. Z. Jhanjhi^3,*, Muneer Ahmad⁴, Mirza Abdur Razzaq¹, Sultan S. Alshamrani⁵, Yasir Malik⁶, Fehmi Jaafar⁷

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3773-3787, 2021, DOI:10.32604/cmc.2021.016001 - 06 May 2021

Abstract Medical data tampering has become one of the main challenges in the field of secure-aware medical data processing. Forgery of normal patients’ medical data to present them as COVID-19 patients is an illegitimate action that has been carried out in different ways recently. Therefore, the integrity of these data can be questionable. Forgery detection is a method of detecting an anomaly in manipulated forged data. An appropriate number of features are needed to identify an anomaly as either forged or non-forged data in order to find distortion or tampering in the original data. Convolutional neural… More >