Enhanced Image Forgery Detection In Digital Images Through Advanced Yolo And Rcnn-Based Deep Learning Techniques
DOI:
https://doi.org/10.29284/mc4rbs49Keywords:
Image Forgery Detection, DL Techniques, YOLO, RCNN, Digital Image ForensicsAbstract
Fake image detection is crucial these days, because any image can be altered easily. In this regard, this research proposes a simpler yet powerful approach that employs state-of-the-art Deep Learning (DL) techniques along with traditional image processing. Combining the powers of You Only Look Once (YOLO) and Recurrent Neural Network (RCNN) for object detection, Discrete Cosine Transform (DCT) for fast frequency domain analysis, and the Watershed algorithm for accurate image segmentation. This study presents a fine blend of traditional techniques with DL, providing better results than the single use of either of the two mentioned above. The proposed technique has been empirically tested on three public standard datasets: CASIA 2.0, CoMoFoD, and MICC-F2000. The proposed technique achieves an accuracy of 98%, 99%, and 97%, respectively. The superior results prove that this technique is not only strong but also reliable; thus, it fulfills the demand for its potential use in the real world for digital image forensics and security. Accordingly, this work presents a full framework that merges modern DL approaches with traditional image processing to accurately detect forged areas in digital photos and meets an important demand of today's digital world.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
