PFDNET: A DEEP LEARNING APPROACH FOR ROBUST SHARED PHOTO AUTHENTICATION AND TAMPER RECOVERY

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Mr.D.JEEVA; MS.G.MAHALAKSHMI

PFDNET: A DEEP LEARNING APPROACH FOR ROBUST SHARED PHOTO AUTHENTICATION AND TAMPER RECOVERY

Authors: Mr.D.JEEVA, MS.G.MAHALAKSHMI

Unique Paper ID: 179759
Volume: 11
Issue: 12
PageNo: 7791-7794

Keywords: No Keywords Found

Abstract:
deep learning-based framework called the Photo Forgery Detection Network (PFDNet) was created to combat image tampering by providing lossless recovery in addition to detection. To ensure content consistency, it incorporates a Cyber Vaccinator module that uses the original image and edge map to create an immunized image version. The Invertible Neural Network-based Forgery Detector module uses a forward pass to identify tampered areas and a backward pass to retrieve the original data. By contrasting the original and restored images, Run-Length Encoding (RLE) verifies the recovery. The shortcomings of conventional techniques are successfully addressed by PFDNet, which excels at processing low-resolution or compressed images while guaranteeing robustness, authenticity, and high fidelity in digital image integrity on online platforms.

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ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 7791-7794

PFDNET: A DEEP LEARNING APPROACH FOR ROBUST SHARED PHOTO AUTHENTICATION AND TAMPER RECOVERY

Available:https://ijirt.org/article?manuscript=179759

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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