Preserving Visual Authenticity: Block chain-Augmented AI Frameworks for Advanced Digital Deception Recognition and Mitigation
M. Priya, J Murugesan, P Bhuvaneswari, M Rubigha, S Lalithambikai, B Mohanraj
Abstract
The rapid advancements in deep learning have given rise to sophisticated DeepFake technologies, posing significant threats to visual integrity and authenticity in digital media. This paper presents an innovative approach to DeepFake detection and mitigation by integrating blockchain technology with artificial intelligence frameworks. The proposed Blockchain-Augmented AI (BAAI) framework utilizes the immutability and decentralized nature of block chain to enhance the security and reliability of the detection process. Our method involves the development of advanced AI models for detecting DeepFakes, which are then integrated with a blockchain-based ledger to ensure the verifiability and traceability of detection results. In this proposed work, a novel integration of blockchain technology and AI designed to enhance DeepFake detection capabilities. The framework achieves a $97 \%$ accuracy rate, ensuring reliable identification of manipulated media, while maintaining a low false positive rate of 3%. These results highlight the BAAI framework’s effectiveness in minimizing erroneous detections and its robustness in safeguarding digital visual content. In the face of increasingly sophisticated DeepFake technologies, this framework offers a crucial advancement in combating digital deception.