Blockchain-Integrated Generative AI Framework for Robust Deepfake Detection and Digital Media Verification

Abstract

Due to the fast emergence of deepfake technologies, the authenticity of digital media is under serious threat, and issues such as misinformation, identity fraud, and reputation damage, are encountered. Older deepfake detection techniques (based on pattern recognition or supervised learning) have poor capabilities to identify very state-of-the-art fake content and do not have a way to confirm the origin of content. This research introduces a new Blockchain-Integrated Generative AI model, which is the solution to these drawbacks, integrating Non-Fungible Token (NFT)-based media provenance with Generative AI-powered content analysis. Within this system, original digital media is initially stored on a blockchain and given a unique NFT that establishes an indelible and traceable record of origin of content. Then, a Generative Adversarial Network (GAN) evaluates the media to reveal minor anomalies, including pixel-level anomalies, unnatural facial expressions, or lighting anomalies, to show that the media is manipulated. The proposed system guarantees the authenticity and integrity of digital information by combining the NFT-based verification with artificial intelligence-based anomaly detection. Initial analyses indicate that this two-layered solution can greatly increase the detection efficiency relative to the traditional solutions besides offering safe provenance verification. The suggested framework provides a scalable, real-time system to verify digital media, which reduces the volume of threats posed by deepfakes and inspires trust in the Internet ecosystem. The study helps to build a strong basis of safe digital content handling and prevent the increasing issues of the fake media.