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A Survey On Video Forgery Detection  [PDF]
Sowmya K. N.,H. R. Chennamma
Computer Science , 2015,
Abstract: The Digital Forgeries though not visibly identifiable to human perception it may alter or meddle with underlying natural statistics of digital content. Tampering involves fiddling with video content in order to cause damage or make unauthorized alteration/modification. Tampering detection in video is cumbersome compared to image when considering the properties of the video. Tampering impacts need to be studied and the applied technique/method is used to establish the factual information for legal course in judiciary. In this paper we give an overview of the prior literature and challenges involved in video forgery detection where passive approach is found.
Copy- Move Attack Forgery Detection by Using SIFT  [PDF]
Mr. Swapnil H. Kudke,,Dr. Avinash D. Gawande
International Journal of Innovative Technology and Exploring Engineering , 2013,
Abstract: Due to rapid advances and availabilities of powerful image processing software’s, it is easy to manipulate and modify digital images. So it is very difficult for a viewer to judge the authenticity of a given image. Nowadays, it is possible to add or remove important features from an image without leaving any obvious traces of tampering. As digital cameras and video cameras replace their analog counterparts, the need for authenticating digital images, validating their content and detecting forgeries will only increase. For digital photographs to be used as evidence in law issues or to be circulated in mass media, it is necessary to check the authenticity of the image. So In this paper, describes an Image forgery detection method based on SIFT. In particular, we focus on detection of a special type of digital forgery – the copy-move attack, in a copy-move image forgery method; a part of an image is copied and then pasted on a different location within the same image. In this approach an improved algorithm based on scale invariant features transform (SIFT) is used to detect such cloning forgery, In this technique Transform is applied to the input image to yield a reduced dimensional representation, After that Apply key point detection and feature descriptor along with a matching over all the key points. Such a method allows us to both understand if a copy–move attack has occurred and, also furthermore gives output by applying clustering over matched points.
Suitability of Independent Component Analysis in Digital Image Forgery Detection
Sunil Kumar,J.V. Desai,S. Mukherjee,P. K. Das
International Journal of Engineering and Technology , 2013,
Abstract: Digital image forgery detection is one of the hot research area in the recent time. A lot of researchers are trying different tools to establish the authenticity of a given image. There can be many types of forgery performed on digital image. Watermarking is one of the traditional techniques to detect any type of tampering with the original image, but that has to be done at the time of capturing the image. Once an image has been captured without such technique, there is no alternate but to apply different blind forgery detection techniques. The present paper is an effort to explore Independent Component Analysis (ICA) as a tool to get clues about the tampering with original image. The results may provide further leads to the researchers working in the same area.
Copy-Move Forgery Detection in Digital Images: Progress and Challenges
Sunil Kumar,P. K. Das,Shally,S. Mukherjee
International Journal on Computer Science and Engineering , 2011,
Abstract: With the advancement of technology and easy availability of imaging tools, it’s not difficult now a days to manipulate digital images to hide or create misleading images. Image forgery detection is currently one of the hot research fields of image processing. Many research papers have been published during recent years. Image forgery has already been categorized. Copy-Move forgery is one of thefrequently used techniques. In this paper a review of the existing techniques has been done. An attempt has been made in this paper to list and highlight almost all the proposed methods along with their key features.
Detection of Copy-Move Forgery of Images Using Discrete Wavelet Transform  [PDF]
Preeti Yadav,Yogesh Rathore
International Journal on Computer Science and Engineering , 2012,
Abstract: Digital images are used everywhere and it is easy to manipulate and edit because of availability of various image processing and editing software. In a copy-move image forgery, a part of an image is copiedand then pasted on a different location within the same image. A copy-move image forgery is done either for hiding some image object, or adding more details resulting in at least some part being cloned. In both the case, image reliability is lost. In this paper an improved algorithm based on Discrete Wavelet Transform (DWT) is used to detect such cloning forgery. In this technique at first DWT (Discrete Wavelet Transform) is applied to the input image for a reduced dimensional representation. Then the compressed image isdivided into overlapping blocks. After that Lexicographic sorting is performed, and duplicated blocks are identified. Due to DWT usage, detection is first carried out on lowest level image representation. This approach increases accuracy of detection process and reduces the time needed for the detection process.
Fast Transforms in Image Processing: Compression, Restoration, and Resampling  [PDF]
Leonid P. Yaroslavsky
Advances in Electrical Engineering , 2014, DOI: 10.1155/2014/276241
Abstract: Transform image processing methods are methods that work in domains of image transforms, such as Discrete Fourier, Discrete Cosine, Wavelet, and alike. They proved to be very efficient in image compression, in image restoration, in image resampling, and in geometrical transformations and can be traced back to early 1970s. The paper reviews these methods, with emphasis on their comparison and relationships, from the very first steps of transform image compression methods to adaptive and local adaptive filters for image restoration and up to “compressive sensing” methods that gained popularity in last few years. References are made to both first publications of the corresponding results and more recent and more easily available ones. The review has a tutorial character and purpose. 1. Introduction: Why Transforms? Which Transforms? It will not be an exaggeration to assert that digital image processing came into being with introduction, in 1965 by Cooley and Tukey, of the Fast Fourier Transform algorithm (FFT, [1]) for computing the Discrete Fourier Transform (DFT). This publication immediately resulted in impetuous growth of activity in all branches of digital signal and image processing and their applications. The second wave in this process was inspired by the introduction into communication engineering and digital image processing, in the 1970s, of Walsh-Hadamard transform and Haar transform [2] and the development of a large family of fast transforms with FFT-type algorithms [3–5]. Whereas Walsh-Hadamard and Haar transforms have already been known in mathematics, other transforms, for instance, quite popular at the time Slant Transform [6], were being invented “from scratch.” This development was mainly driven by the needs of data compression, though the usefulness of transform domain processing for image restoration and enhancement was also recognized very soon [3]. This period ended up with the introduction of the Discrete Cosine Transform (DCT, [7, 8]), which was soon widely recognized as the best choice among all available at the time transforms and resulted in JPEG and MPEG standards for image, audio, and video compression. The third large wave of activities in transforms for signal and image processing was caused by the introduction, in the 1980s, of a family of transforms that was coined the name “wavelet transform” [9]. The main motivation was achieving a better local representation of signals and images in contrast to the “global” representation that is characteristic to Discrete Fourier, DCT, Walsh-Hadamard, and other fast transforms
Comparision and analysis of photo image forgery detection techniques  [PDF]
S. Murali,Govindraj B. Chittapur,Prabhakara H. S,Basavaraj S. Anami
Computer Science , 2013, DOI: 10.5121/ijcsa.2012.2605
Abstract: Digital Photo images are everywhere, on the covers of magazines, in newspapers, in courtrooms, and all over the Internet. We are exposed to them throughout the day and most of the time. Ease with which images can be manipulated; we need to be aware that seeing does not always imply believing. We propose methodologies to identify such unbelievable photo images and succeeded to identify forged region by given only the forged image. Formats are additive tag for every file system and contents are relatively expressed with extension based on most popular digital camera uses JPEG and Other image formats like png, bmp etc. We have designed algorithm running behind with the concept of abnormal anomalies and identify the forgery regions.
Forgery Detection and Value Identification of Euro Banknotes  [PDF]
Arcangelo Bruna,Giovanni Maria Farinella,Giuseppe Claudio Guarnera,Sebastiano Battiato
Sensors , 2013, DOI: 10.3390/s130202515
Abstract: This paper describes both hardware and software components to detect counterfeits of Euro banknotes. The proposed system is also able to recognize the banknote values. Differently than other state-of-the-art methods, the proposed approach makes use of banknote images acquired with a near infrared camera to perform recognition and authentication. This allows one to build a system that can effectively deal with real forgeries, which are usually not detectable with visible light. The hardware does not use any mechanical parts, so the overall system is low-cost. The proposed solution is reliable for ambient light and banknote positioning. Users should simply lean the banknote to be analyzed on a flat glass, and the system detects forgery, as well as recognizes the banknote value. The effectiveness of the proposed solution has been properly tested on a dataset composed by genuine and fake Euro banknotes provided by Italy's central bank.
An Efficient Method for Detection of Copy-Move Forgery Using Discrete Wavelet Transform
Er. Saiqa Khan,Er. Arun Kulkarni
International Journal on Computer Science and Engineering , 2010,
Abstract: Copy-Move forgery is a specific type of image forgery, in which a part of digital image is copied and pasted to another part in the same image. This paper describes blind forensics approach for detecting Copy-Move forgery. Our technique works by first applying DWT (Discrete Wavelet Transform) to the input image to yield a reduced dimension representation [1]. Then the compressed image is divided into overlapping blocks. These blocks are then sorted and duplicated blocks are identified using Phase Correlation as similarity criterion. Due to DWT usage, detection is first carried out on lowest level image representation. This approach drastically reduces the time needed for the detection process.
An Evaluation of Popular Copy-Move Forgery Detection Approaches  [PDF]
Vincent Christlein,Christian Riess,Johannes Jordan,Corinna Riess,Elli Angelopoulou
Computer Science , 2012, DOI: 10.1109/TIFS.2012.2218597
Abstract: 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.
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