Emad Ul Haq Qazi^1,*, Tanveer Zia¹, Muhammad Imran², Muhammad Hamza Faheem¹

Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 225-240, 2023, DOI:10.32604/iasc.2023.041181

Abstract Deep learning is considered one of the most efficient and reliable methods through which the legitimacy of a digital image can be verified. In the current cyber world where deepfakes have shaken the global community, confirming the legitimacy of a digital image is of great importance. With the advancements made in deep learning techniques, now we can efficiently train and develop state-of-the-art digital image forensic models. The most traditional and widely used method by researchers is convolution neural networks (CNN) for verification of image authenticity but it consumes a considerable number of resources and requires a large dataset for training.… More >

Ala’a R. Al-Shamasneh¹, Rabha W. Ibrahim^2,3,4,*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 795-810, 2024, DOI:10.32604/cmc.2023.042755

Abstract The growing prevalence of fake images on the Internet and social media makes image integrity verification a crucial research topic. One of the most popular methods for manipulating digital images is image splicing, which involves copying a specific area from one image and pasting it into another. Attempts were made to mitigate the effects of image splicing, which continues to be a significant research challenge. This study proposes a new splicing detection model, combining Sonine functions-derived convex-based features and deep features. Two stages make up the proposed method. The first step entails feature extraction, then classification using the “support vector… More >

Arslan Akram^1,3, Javed Rashid^2,3,4, Arfan Jaffar¹, Fahima Hajjej⁵, Waseem Iqbal⁶, Nadeem Sarwar^7,*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 145-166, 2024, DOI:10.32604/cmc.2023.041074

Abstract Today’s forensic science introduces a new research area for digital image analysis for multimedia security. So, Image authentication issues have been raised due to the wide use of image manipulation software to obtain an illegitimate benefit or create misleading publicity by using tempered images. Exiting forgery detection methods can classify only one of the most widely used Copy-Move and splicing forgeries. However, an image can contain one or more types of forgeries. This study has proposed a hybrid method for classifying Copy-Move and splicing images using texture information of images in the spatial domain. Firstly, images are divided into equal… More >

Jie Zhang, Jianxun Zhang^*, Bowen Li, Jie Cao, Yifan Guo

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2883-2895, 2023, DOI:10.32604/iasc.2023.039805

Abstract With the improvement of image editing technology, the threshold of image tampering technology decreases, which leads to a decrease in the authenticity of image content. This has also driven research on image forgery detection techniques. In this paper, a U-Net with multiple sensory field feature extraction (MSCU-Net) for image forgery detection is proposed. The proposed MSCU-Net is an end-to-end image essential attribute segmentation network that can perform image forgery detection without any pre-processing or post-processing. MSCU-Net replaces the single-scale convolution module in the original network with an improved multiple perceptual field convolution module so that the decoder can synthesize the… More >

M. Muzaffar Hameed^1,2, Rodina Ahmad^1,*, Laiha Mat Kiah¹, Ghulam Murtaza³, Noman Mazhar¹

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1267-1289, 2023, DOI:10.32604/cmc.2023.035063

Abstract Offline signature verification (OfSV) is essential in preventing the falsification of documents. Deep learning (DL) based OfSVs require a high number of signature images to attain acceptable performance. However, a limited number of signature samples are available to train these models in a real-world scenario. Several researchers have proposed models to augment new signature images by applying various transformations. Others, on the other hand, have used human neuromotor and cognitive-inspired augmentation models to address the demand for more signature samples. Hence, augmenting a sufficient number of signatures with variations is still a challenging task. This study proposed OffSig-SinGAN: a deep… More >

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

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 forged features. Our method locates… 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

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 modified by an attacker. The… 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

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 forward a quick and effective… 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 >