Enhancing Deepfake Content Detection Through Blockchain Technology
Qurat-ul-ain Mastoi, Muhammad Faisal Memon, Salman Jan, Atif Jamil, Muhammad Faique, Zeeshan Ali, Abdullah Raza Lakhan, Toqeer Ali Syed · International Journal of Advanced Computer Science and Applications · 2025
Deepfake technology poses a growing threat to the authenticity and trustworthiness of digital media, necessitating the development of advanced detection mechanisms. While AI-based methods have shown promise, they generally face limitations in terms of generalization and scalability. We present a blockchain-enabled watermarking technique, characterized by its immutable, transparent, and decentralized nature, which offers a robust complementary approach for enhancing media authentication through methods such as cryptographic watermarking, decentralized identity, and content provenance tracking. To train and evaluate blockchain-based watermarking and deepfake detection systems, a variety of large-scale datasets are utilized. Video datasets include UADFV (49 real, 49 fake), Deepfake-TIMIT (320 real, 640 fake), DFFD (1000 real, 3000 fake), Celeb-DF v2 (590 real, 5639 fake), DFDC (23,564 real, 104,500 fake), DeeperForensics-1.0 (50,000 real, 10,000 fake), FaceForensics++ (1000 real, 5000 fake), and ForgeryNet (99,630 real, 121,617 fake). Image datasets include DFFD (58,703 real, 240,336 fake), FFHQ (70,000 GAN-generated), iFakeFaceDB (87,000 fake), 100k AI Faces, and over 2.8 million samples in ForgeryNet. Despite integration challenges such as scalability, computational cost, and standardization, blockchain-based solutions show promise in tracking content origin and enhancing verification. Simulation results demonstrate that the proposed blockchain-enabled watermarking achieves a higher accuracy in detecting fake content compared to existing machine learning methods.