Recent Papers

This research focuses on creating a detection system for Deepfakes.especially for photos, taking advantage of computer vision breakthroughs and AI. A growing threat from manipulated images requires a robust Solution to locate and prevent misleading visual content. Our approach resolves the issue of separating real images from manipulated ones with excellent precision. Unlike techniques designed to videobased deepfakes, whereas our model is image-based We acquire distinct collections of realistic and synthetic images, utilize Convolutional Neural Networks (CNNs) which are image-based testing, and tune pre-trained models. The system undergoes meticulous training, monitoring, and adjustment of settings to ensure maximum performance.The goal is to create a reliable system that identifies manipulated images, preserving the integrity of visual content and establishing trust in online media. This system integrates AI and computer vision for defence against image-based forgeries. Applications include determining whether images are authentic in journalism, social media, and e-commerce, and assisting content verifying to ensure photos are credible and reliable across platforms.

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