Deepfake detection and medical image authentication via a blockchain-integrated deep learning framework

Abstract

The recent advances in deepfake technologies have increased the risk of adversarial attacks on clinical content. The existing schemes towards image repositories are mostly designed considering a centralized system to store and verify medical images. However, they are susceptible to tampering and data breaches. This paper presents a blockchain-integrated deep learning (DL) framework for a secure and verifiable medical image authentication process. The first stage presents Efficient-vision-transformer (ViT), a hybrid learning model designed based on the joint approach of a convolutional EfficientNet model and vision transformers for detecting deepfake images. The second stage adopts a blockchain approach where the verified real images are subjected to hash computation and then stored on decentralized storage nodes using the interplanetary file system (IPFS). The experimental results showed an impressive detection accuracy of 99% by Efficient-ViT, and it is robust against various adversarial attacks. The blockchain module further ensures tamper-proofing, transparency, and long-term data integrity.