AI Summary & Key Takeaways
AI InsightThe proposed approach also provides to have reasonable precision and recall values, 92.51% and 96.11%, respectively when compared to existing algorithms.
📌 3 Key Takeaways
Abstract
In this digital age, there has been ever-increasing trend on the amount of digital image data. On the other side, in parallel with this situation, image tampering and manipulation attacks have become widespread by the use of various image editing software packages. It means that digital image data have become more vulnerable to tampering or manipulation through forgeries. Copy-move duplication is a type of image forgery involving the process of copying and then pasting an image part from one region to another location within the same image. This paper proposes a novel enhancement on the copy-move forgery detection algorithms. The proposed strategy is based on adding specific noise on the image in order to reach more robust detection. It is observed from the experimental results that, with the specific noise insertion, the copy-move forgery detection algorithms can obtain better performances in comparison to conventional methods. It is found at image level that, by the proposed strategy, with 25×25 blocks, 94.16% accuracy rate is reached. The proposed approach also provides to have reasonable precision and recall values, 92.51% and 96.11%, respectively when compared to existing algorithms.