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Image Vaccinator an Image Tamper Resilient and Lossesless Auto Recovery using Invertible Neural Network
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B.Hemalatha and N.L.Deepsikha


Rapid advancements in digital tools make image editing easy and free. The main problem is the compromised credibility of digital images due to the ease of manipulation through advanced digital image processing tools. This vulnerability leads to the creation and dissemination of maliciously fabricated images, fostering the potential for misinformation and influencing public opinion, especially on online social networks. Today, people frequently interact with their families, friends, and colleagues through online social networks (OSN). People enjoy posting and sharing their photos in online communities, blogs, and content sharing sites. The problem addressed in this project is the susceptibility of digital images to tampering, which compromises security and privacy. Traditional image forgery detection methods face challenges in reproducing original content after manipulation. This project introduces an advanced Image Immunization System leveraging Invertible Neural Networks. The system, comprising the Cyber Vaccinator, Vaccine Validator, Forward Pass for Tamper Detection, and Backward Pass for Image Self-Recovery, aims to proactively immunize images against various attacks. Run-Length Encoding in the backward pass to transform hidden perturbations into information, facilitating lossless recovery of the authentic image. Keywords—Image Tamper Resilient, Invertible Neural Networks (INN), Vaccine Validator, Pixel Classification Module, User Interface (UI), Run-Length Encoding

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