Comparative Analysis of Techniques Used to Detect Copy-Move Tampering for Real-World Electronic Images

Evolution of high computational powerful computers, easy availability of several innovative editing software package and high-definition quality-based image capturing tools follows to effortless result in producing image forgery. Though, threats for security and misinterpretation of digital images and scenes have been observed to be happened since a long period and also a lot of research has been established in developing diverse techniques to authenticate the digital images. On the contrary, the research in this region is not limited to checking the validity of digital photos but also to exploring the specific signs of distortion or forgery. This analysis would not require additional prior information of intrinsic content of corresponding digital image or prior embedding of watermarks. In this paper, recent growth in the area of digital image tampering identification have been discussed along with benchmarking study has been shown with qualitative and quantitative results. With variety of methodologies and concepts, different applications of forgery detection have been discussed with corresponding outcomes especially using machine and deep learning methods in order to develop efficient automated forgery detection system. The future applications and development of advanced soft-computing based techniques in digital image forgery tampering has been discussed

Comparative Analysis of Techniques Used to Detect Copy-Move Tampering for Real-World Electronic Images

Evolution of high computational powerful computers, easy availability of several innovative editing software package and high-definition quality-based image capturing tools follows to effortless result in producing image forgery. Though, threats for security and misinterpretation of digital images and scenes have been observed to be happened since a long period and also a lot of research has been established in developing diverse techniques to authenticate the digital images. On the contrary, the research in this region is not limited to checking the validity of digital photos but also to exploring the specific signs of distortion or forgery. This analysis would not require additional prior information of intrinsic content of corresponding digital image or prior embedding of watermarks. In this paper, recent growth in the area of digital image tampering identification have been discussed along with benchmarking study has been shown with qualitative and quantitative results. With variety of methodologies and concepts, different applications of forgery detection have been discussed with corresponding outcomes especially using machine and deep learning methods in order to develop efficient automated forgery detection system. The future applications and development of advanced soft-computing based techniques in digital image forgery tampering has been discussed

Forgery detection from printed images: a tool in crime scene analysis

.The preliminary analysis of the genuineness of a photo is become, in the time, the first step of any forensic examination that involves images, in case there is not a certainty of its intrinsic authenticity. Digital cameras have largely replaced film based devices, till some years ago, in some areas (countries) just images made from film negatives where considered fully reliable in Court. There was a widespread prejudicial thought regarding a digital image which, according to some people, it cannot ever been considered a legal proof, since its “inconsistent digital nature”. Great efforts have been made by the forensic science community on this field and now, after all this year, different approaches have been unveiled to discover and declare possible malicious frauds, thus to establish whereas an image is authentic or not or, at least, to assess a certain degree of probability of its “pureness”. Nowadays it’s an easy practice to manipulate digital images by using powerful photo editing tools. In order to alter the original meaning of the image, copy-move forgery is the one of the most common ways of manipulating the contents. With this technique a portion of the image is copied and pasted once or more times elsewhere into the same image to hide something or change the real meaning of it. Whenever a digital image (or a printed image) will be presented as an evidence into a Court, it should be followed the criteria to analyze the document with a forensic approach to determine if it contains traces of manipulation. Image forensics literature offers several examples of detectors for such manipulation and, among them, the most recent and effective ones are those based on Zernike moments and those based on Scale Invariant Feature Transform (SIFT). In particular, the capability of SIFT to discover correspondences among similar visual contents allows the forensic analysis to detect even very accurate and realistic copy-move forgeries. In some situation, however, instead of a digital document only its analog version may be available. It is interesting to ask whether it is possible to identify tampering from a printed picture rather than its digital counterpart. Scanned documents or recaptured printed documents by a digital camera are widely used in a number of different scenarios, from medical imaging, law enforcement to banking and daily consumer use. So, in this paper, the problem of identifying copy-move forgery from a printed picture is investigated. The copy-move manipulation is detected by proving the presence of copy-move patches in the scanned image by using the tool, named CADET (Cloned Area DETector), based on our previous methodology which has been adapted in a version tailored for printed image case (e.g. choice of the minimum number of matched keypoints, size of the input image, etc.) In this paper a real case of murder is presented, where an image of a crime scene, submitted as a printed documentary evidence, had been modified by the defense advisors to reject the theory of accusation given by the Prosecutor. The goal of this paper is to experimentally investigate the requirement set under which reliable copy-move forgery detection is possible on printed images, in that way the forgery test is the very first step of an appropriate operational check list manual

An Efficiency Enhanced Cluster Expanding Block Algorithm for Copy-Move Forgery Detection

[[conferencetype]]國際[[conferencetkucampus]]台北校園[[conferencedate]]20150902~20150904[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Taipei, Taiwa

Robust Copy-move Forgery Detection through Invariant Moment Features

[[notice]]補正完畢[[conferencedate]]20161114~2016111

An Evaluation of Popular Copy-Move Forgery Detection Approaches

A copy-move forgery is created by copying and pasting content within the same image, and potentially post-processing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.Comment: Main paper: 14 pages, supplemental material: 12 pages, main paper appeared in IEEE Transaction on Information Forensics and Securit

Copy move forgery detection using key point localized super pixel based on texture features

The most important barrier in the image forensic is to ensue a forgery detection method such can detect the copied region which sustains rotation, scaling reflection, compressing or all. Traditional SIFT method is not good enough to yield good result. Matching accuracy is not good. In order to improve the accuracy in copy move forgery detection, this paper suggests a forgery detection method especially for copy move attack using Key Point Localized Super Pixel (KLSP). The proposed approach harmonizes both Super Pixel Segmentation using Lazy Random Walk (LRW) and Scale Invariant Feature Transform (SIFT) based key point extraction. The experimental result indicates the proposed KLSP approach achieves better performance than the previous well known approaches