Copy-move forgery detection using binary discriminant features
| Autor: | Madhu S. Nair, Priya Mariam Raju |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Matching (statistics)
Color histogram Similarity (geometry) General Computer Science Computer science 0211 other engineering and technologies ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Copy-move forgery 02 engineering and technology Digital image Discriminative model Image forgery 0202 electrical engineering electronic engineering information engineering Feature detection (computer vision) 021110 strategic defence & security studies business.industry Image forensics Pattern recognition QA75.5-76.95 Binary discriminant feature descriptor Feature (computer vision) Electronic computers. Computer science Block-based approach Superpixel based approach 020201 artificial intelligence & image processing Artificial intelligence F1 score business |
| Zdroj: | Journal of King Saud University: Computer and Information Sciences, Vol 34, Iss 2, Pp 165-178 (2022) |
| ISSN: | 1319-1578 |
| Popis: | Digital images have numerous applications in various fields such as cyber forensics, courts, newspapers, magazines, medical field and many more. Therefore, the trustfulness of the contents of an image is very essential. Image manipulation has become very much simple and common due to the development of high resolution digital cameras, powerful computers and numerous image editing software. Hence, falsification of visual content of images is not at all limited to specialists and detection of forged images have become a highly challenging task. Of the different kinds of image forgery, the proposed method deals with one particular kind known as copy-move forgery. The method is an integration of the conventional block-based and keypoint based methods. The method first identifies the forged keypoint locations and then localizes the forged region using a region extraction technique. To improve the accuracy of detection, a Binary Discriminative Feature descriptor (BDF) is used for feature detection and matching. The suspected regions are identified by replacing the matching feature points with corresponding superpixel blocks. The neighbouring blocks are then merged with the suspected regions based on color similarity, using Color Histogram Matching and a final morphological close operation extracts the detected forged regions. Experimental results show that the proposed method has better detection accuracy, in terms of precision, recall and F1 Score, when compared with the state-of-the-art methods of copy-move forgery detection, under conditions of plain copy-move forgery as well as post-processing conditions like brightness changes, contrast adjustments, color reduction and blurring. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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