Abstract: The rising popularity of digital image sharing requires verification systems for visual content authenticity. Digital media reliability suffers because of image tampering that takes place on social platforms. The current detection approaches fail with degraded images while unable to restore lost content. This research presents PFDNet as a deep learning-based framework which detects photo tampering and restores authentic content through its framework. The Cyber Vaccinator module generates a tamper-proof updated image through integration of the actual content with edge information. The Invertible Neural Network (INN) performs alteration detection in its forward process and restores original content in its backward process. The accuracy verification function of Run-Length Encoding (RLE) exists for restoration purpose. The experiment results demonstrate PFDNet successfully recognizes tampered images while restoring them faithfully and maintaining their authenticity.
Keywords: Photo Forgery Detection, PFDNet, Image Tampering, Deep Learning, Cyber Vaccinator, Tamper Resistance
|
DOI:
10.17148/IJARCCE.2025.14505
