Fusion Approaches System of Copy-Move Forgery Detection

American Journal of Computer Science and Engineering Survey (AJCSES) is a peer review open access journal publishing the state of art research in computer science and engineering survey.

Image forgery detection approaches are varied and serve same objectives. However, the difference in image properties causes some limitations of most of these approaches. Integrate multiple forensic approaches to increase the efficiency of detecting and localize the forgery was proposed based on the same image input source.

In this paper, we propose a new detector algorithm based on different image source format. We propose a fusion approach to detect a copy-move forgery based on Patch Match enhanced by the dense field technique, and sensor pattern noise based on photo response non-uniformity (PRNU).

The F-measure score used same evaluation function to make the system more robust. The output result shows high efficiency of detecting and localizing the forgery in different image formats, for both passive and active forgery detection.

The Copy- move forgery is widely used, and because it can be done very proficiently by beginners. On the other hand, detecting this type of forgery is difficult and it is not guaranteed. There are two intensive challenges for most CMFD algorithms.

First one when the copy-move is done by using the background to hide some seen in the image. This case can be detected by using Patch Matching of the offset points in the forged image. The other challenge case when the copy-move done by rescale the copied part and baste it on same location to make it more visible, for instance.

This case of forgery requires the original image and PRNU approach will be the best way to detect that type of forgery. The experiment shows that the evaluation is variant even for same image when the color or the resolution change result different F-score.

 However, F-score overall shoes high efficiency when we used the fusion technique, indeed, we were able to detect different Copy-move forgery format. For future work, we will apply the same concept to forged video and compare the F-score result with litterateur.

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