Authors :
Harshitha V.; Deepthi C. S.; Venkatesh G.; Sahana G. P.
Volume/Issue :
Volume 11 - 2026, Issue 7 - July
Google Scholar :
https://tinyurl.com/dpm3a3e9
Scribd :
https://tinyurl.com/2az6k77w
DOI :
https://doi.org/10.38124/ijisrt/26jul821
Note : A published paper may take 4-5
working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and
ResearchGate.
Abstract :
Fast developments in the field of artificial intelligence have made possible the emergence of hyper-realistic
synthesized media dubbed deepfakes that constitute a significant challenge for digital trust, public communications, and
forensics. Current solutions for detecting media deepfakes employ deep learning models that look for visual and temporal
inconsistencies in altered images and videos. But these methods are reactive and cannot keep up with the development of
the adversarial models meant to circumvent the detection algorithms. In order to overcome this problem, this paper offers
a proactive solution that utilizes Public Key Infrastructure (PKI) and blockchain technology for establishing authenticity
of the media at its moment of capturing. In particular, the solution hashes the original media with a SHA-256 hash
function and saves it on a blockchain compatible with the Ethereum Virtual Machine (EVM) using smart contracts.
Verification of the media involves re-computing its hash and comparing it to the record in the blockchain. Any
modifications to the media will be revealed during the process. A proof-of-concept implementation is used to assess the
framework in terms of transaction time, gas consumed, and robustness to post-production modifications. Experiments
show that the proposed approach allows achieving zero-trust, low-complexity and secure verification of the content
integrity and strengthening digital journalism.
Keywords :
Deepfake Detection, Blockchain, Media Provenance, SHA-256, Smart Contracts, Public Key Infrastructure (PKI), Digital Forensics, Zero-Trust Verification, Ethereum Virtual Machine (EVM), Artificial Intelligence.
References :
- F. Marra, D. Gragnaniello, D. Cozzolino, and L. Verdoliva, "Detection of GAN-generated fake images over social networks," IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 384-389, 2018.
- Coalition for Content Provenance and Authenticity (C2PA), "Technical Specifications for Digital Asset Provenance and Integrity," v2.4, 2024.
- S. Haber and W. S. Stornetta, "How to time-stamp a digital document," Journal of Cryptology, vol. 3, no. 2, pp. 99-111, 1991.
- V. Buterin, "Ethereum: A next-generation smart contract and decentralized application platform," white paper, 2014.
Fast developments in the field of artificial intelligence have made possible the emergence of hyper-realistic
synthesized media dubbed deepfakes that constitute a significant challenge for digital trust, public communications, and
forensics. Current solutions for detecting media deepfakes employ deep learning models that look for visual and temporal
inconsistencies in altered images and videos. But these methods are reactive and cannot keep up with the development of
the adversarial models meant to circumvent the detection algorithms. In order to overcome this problem, this paper offers
a proactive solution that utilizes Public Key Infrastructure (PKI) and blockchain technology for establishing authenticity
of the media at its moment of capturing. In particular, the solution hashes the original media with a SHA-256 hash
function and saves it on a blockchain compatible with the Ethereum Virtual Machine (EVM) using smart contracts.
Verification of the media involves re-computing its hash and comparing it to the record in the blockchain. Any
modifications to the media will be revealed during the process. A proof-of-concept implementation is used to assess the
framework in terms of transaction time, gas consumed, and robustness to post-production modifications. Experiments
show that the proposed approach allows achieving zero-trust, low-complexity and secure verification of the content
integrity and strengthening digital journalism.
Keywords :
Deepfake Detection, Blockchain, Media Provenance, SHA-256, Smart Contracts, Public Key Infrastructure (PKI), Digital Forensics, Zero-Trust Verification, Ethereum Virtual Machine (EVM), Artificial Intelligence.
