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COVERAGE — A novel database for copy-move forgery detection
2016 IEEE International Conference on Image Processing (ICIP), 2016We present COVERAGE — a novel database containing copy-move forged images and their originals with similar but genuine objects. COVERAGE is designed to highlight and address tamper detection ambiguity of popular methods, caused by self-similarity within natural images.
Bihan Wen +5 more
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Detection of copy move forgery using Legendre Moments
2016 24th Signal Processing and Communication Application Conference (SIU), 2016Today there are different manipulations on images. Copy move forgery is one of them. Copy move forgery means that; any part of an image is taken and added to another part of the same image in a professional way. To detect forgeries, image is divided into overlapped blocks and feature vectors are generated for every block. Feature vectors are tips about
Samet Aymaz, Seyma Aymaz, Güzin Ulutas
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A two-stage localization for copy-move forgery detection
Information Sciences, 2018Abstract A two-stage localization for copy-move forgery detection (CMFD) is proposed in this paper. In the first stage, rough localization, Simple Linear Iterative Clustering (SLIC) is employed to segment the image into meaningful patches (superpixels).
Chi-Man Pun, Jim-Lee Chung
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JPEG compression model in copy-move forgery detection
2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), 2017The integrity of visual data is important for the credibility of news media and especially when used as an evidence in court or during criminal investigation. The common way to manipulate image content is copying an object and pasting in another location of the same image.
Adam Novozámský, Michal Sorel
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Copy-move forgery detection based on deep learning
2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2017Copy-move forgery detection (CMFD) is probably one of the most active research areas within the blind image forensics field. Among existing algorithms, most of them are based on block and key-point methods, or combination of them. Recently, some deep convolutional neural networks methods have been applied in the image classification, image forensic ...
Junlin Ouyang, Yizhi Liu, Miao Liao
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A Non-intrusive Method for Copy-Move Forgery Detection
2011The issue of verifying the authenticity and integrity of digital images is becoming increasingly important. Copy-move forgery is one type of image tempering that is commonly used for manipulating digital content; in this case, some part of an image is copied and pasted on another region of the image. Using a non-intrusive approach to solve this problem
Najah Muhammad +3 more
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Efficient Dense-Field Copy–Move Forgery Detection
IEEE Transactions on Information Forensics and Security, 2015We propose a new algorithm for the accurate detection and localization of copy–move forgeries, based on rotation-invariant features computed densely on the image. Dense-field techniques proposed in the literature guarantee a superior performance with respect to their keypoint-based counterparts, at the price of a much higher processing time, mostly due
COZZOLINO, DAVIDE +2 more
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Survey on image copy-move forgery detection
Multimedia Tools and Applications, 2023Mayank Verma, Durgesh Singh 0001
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Copy-move forgery detection from printed images
SPIE Proceedings, 2014Counterfeiting digital images through a copy-move forgery is one of the most common ways of manipulating the semantic content of a picture, whereby a portion of the image is copy-pasted elsewhere into the same image. It could happen, however, instead of a digital image only its analog version may be available. Scanned or recaptured (by a digital camera)
Irene Amerini +5 more
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A review on copy move image forgery detection techniques
2014 IEEE 11th International Multi-Conference on Systems, Signals & Devices (SSD14), 2014With the advent of powerful image editing tools, manipulating images and changing their content is becoming a trivial task. It is now possible to add, modify, or remove important features from an image without leaving any perceptual traces of tampering.
Muhammad Ali Qureshi, Mohamed A. Deriche
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