Abstract
Copy-move forgery is the most predominant forgery technique in the field of digital image forgery. Block-based and interest-based are currently the two mainstream categories for copy-move forgery detection methods. However, block-based algorithm lacks the ability to resist affine transformation attacks, and interest point-based algorithm is limited to accurately locate the tampered region. To tackle these challenges, a coarse-to-fine model (CFM) is proposed. By extracting features, affine transformation matrix and detecting forgery regions, the localization of tampered areas from sparse to precise is realized. Specifically, in order to further exactly extract the forged regions and improve performance of the model, a two-level local search algorithm is designed in the refinement stage. In the first level, the image blocks are used as search units for feature matching, and the second level is to refine the edge of the region at pixel level. The method maintains a good balance between the complexity and effectiveness of forgery detection, and the experimental results show that it has a better detection effect than the traditional interest-based copy and move forgery detection method. In addition, CFM method has high robustness on postprocessing operations, such as scaling, rotation, noise, and JPEG compression.
Highlights
With the rapid development of technology worldwide, there are many ways to obtain and process images [1]
By considering the balance between algorithm complexity and performance to more accurately extract the forgery region, we propose a local search algorithm that can be applied at the image block level and pixel level. e role of the local search algorithm is described in Figure 5, where the grid is used to replace the test image, the region outlined in red is the forged region, and the blue small block is the forged unit; when the first search algorithm is used, the forged unit is an image block, and the second forged unit is a pixel
Compared to Bi [22] and Chen [23], F1 score is slightly lower than them. e possible reason is that the proposed model is based on block and interest point, which focuses more on recall rate. ese results show that the proposed method is more effective than others
Summary
With the rapid development of technology worldwide, there are many ways to obtain and process images [1]. Evolutions in computer technology, the Internet, and image applications have allowed individuals to tamper with image content. Copy-move is the most common means of image forgery, in which a copy of a region is inserted into the same image. As one of the most common means of image tampering, copy-move forgeries may be accompanied by certain postprocessing, including JPEG compression, noise addition, and blurring, to change the image content and confuse the information recipient [2]. Erefore, the passive forensics of copy-move tampered images faces great technical challenges and has a strong practical application value. (2) To further extract the forgery region accurately, a two-stage local search algorithm is designed in the refinement stage to better maintain the balance. (3) e method has better detection results and higher robustness to postprocessing operations such as scaling, rotation, noise, and JPEG compression
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