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@article{179759,
        author = {Mr.D.JEEVA and MS.G.MAHALAKSHMI},
        title = {PFDNET: A DEEP LEARNING APPROACH FOR ROBUST SHARED PHOTO AUTHENTICATION AND TAMPER RECOVERY},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {7791-7794},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179759},
        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.},
        keywords = {},
        month = {May},
        }