Advancements in CNN-Based Techniques for Robust Image Forgery Detection: Challenges and Future Directions

Abstract

In response to the rise of manipulated images online, which are often obscured by compression and resizing, effective forgery detection systems have become essential. These systems address both social challenges on platforms like Facebook and legal issues stemming from image manipulation. A common form of image tampering, known as copy-move forgery, involves duplicating a part of an image and pasting it elsewhere within the same image. This study presents a novel approach utilizing block processing and feature extraction to tackle the detection challenges posed by such forgeries. By analyzing features from transforms applied to image blocks, the proposed system can accurately detect manipulations. Incorporating Convolutional Neural Networks (CNNs) further enhances detection efficiency through convolution and pooling layers, enabling the system to distinguish between genuine and tampered images. Evaluation on the CASIA2 dataset, consisting of 4795 images (1701 authentic and 3274 forged), demonstrates a high accuracy of 97.7%, marking significant progress toward reliable and real-time forgery detection. This methodology offers a robust solution for addressing copy-move forgeries, providing a valuable tool to combat image manipulation in social and legal domains.