The local features include position and texture information of salient regions in the image. The global features are based on Zernike moments representing luminance and chrominance characteristics of the image as a whole. Both global and local features are used in forming the hash sequence. Ī robust hashing method is developed for detecting image forgery including removal, insertion, and replacement of objects, and abnormal color modification, and for locating the forged area. In this paper we present database organization and content, creation of forged images, postprocessing methods, and database testing.
Also, postprocessing methods, such as JPEG compression, blurring, noise adding, color reduction etc., are applied at all forged and original images.
Images are grouped in 5 categories according to applied manipulation: translation, rotation, scaling, combination and distortion. Every image set includes forged image, two masks and original image. We developed new database for a CMFD that consist of 260 forged image sets. Numerous algorithms have been proposed for a copy-move forgery detection (CMFD), but there exist only few benchmarking databases for algorithms evaluation. One of the common forgery method is a copy-move forgery, where part of an image is copied to another location in the same image with the aim of hiding or adding some image content. Experimental results on complex data sets, comparing the proposed techniques with a gamut of copy-move detection approaches and other fusion methodologies in the literature, show the effectiveness of the proposed method and its suitability for real-world applications.ĭue to the availability of many sophisticated image processing tools, a digital image forgery is nowadays very often used. Finally, we propose different techniques that exploit the multi-directionality of the data to generate the final outcome detection map in a machine learning decision-making fashion. Afterward, the conditional probabilities missing entries are properly estimated through generative models applied on the existing training data. In this paper, we go beyond traditional forgery detectors and aim at combining different properties of copy-move detection approaches by modeling the problem on a multiscale behavior knowledge space, which encodes the output combinations of different techniques as a priori probabilities considering multiple scales of the training data. The detection of copy-move image tampering is of paramount importance nowadays, mainly due to its potential use for misleading the opinion forming process of the general public.
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