Combating Deepfake Videos Using Blockchain and Smart Contracts
Open accessAbstract
With the rise of artificial intelligence (AI) and deep learning techniques, fake digital contents have proliferated in recent years. Fake footage, images, audios, and videos (known as deepfakes) can be a scary and dangerous phenomenon and can have the potential of altering the truth and eroding trust by giving false reality. Proof of authenticity (PoA) of digital media is critical to help eradicate the epidemic of forged content. Current solutions lack the ability to provide history tracking and provenance of digital media. In this paper, we provide a solution and a general framework using Ethereum smart contracts to trace and track the provenance and history of digital content to its original source even if the digital content is copied multiple times. The smart contract utilizes the hashes of the interplanetary file system (IPFS) used to store digital content and its metadata. Our solution focuses on video content, but the solution framework provided in this paper is generic enough and can be applied to any other form of digital content. Our solution relies on the principle that if the content can be credibly traced to a trusted or reputable source, the content can then be real and authentic. The full code of the smart contract has been made publicly available at Github.
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Other researchers focus on content to categorize fake news by distinguishing different formats and content types of data in the news.
“However, other researchers (Parikh and Atrey 2018; Fraga-Lamas and Fernández-Caramés 2020; Hasan and Salah 2019; Masciari et al. 2020; Bakdash et al. 2018; Elhadad et al. 2019; Yang et al. 2019b) focus on the content to categorize types of fake news through distinguishing the different formats and content types of data in the news (e.g., text and/or multimedia).”
From Fake news, disinformation and misinformation in social media: a review (2023) · cites this paper as Hasan and Salah 2019
The Introduction identifies fake videos, images, paintings, and audio as forms of digital content, and the Abstract says the paper focuses on video. But the paper proposes tracing content provenance with blockchain; it does not categorize fake news by distinguishing text from multimedia or other content formats. (LLM, conf=0.84)
Experimental — accuracy not yet measuredA security analysis of Hasan and Salah's Blockchain-based solution shows it addresses integrity, accountability, authorization, availability, and non-repudiation.
“Additionally, a security analysis of their BC-based solution shows how it addresses important security objectives like integrity, accountability, authorization, availability, and non-repudiation.”
From Deepfakes: current and future trends (2024) · cites this paper as Hasan and Salah 2019
Content can be real and authentic if it can be reliably linked to a reliable or trustworthy source.
“Their answer is based on the idea that content can be real and authentic if it can be reliably linked to a reliable or trustworthy source.”
From Deepfakes: current and future trends (2024) · cites this paper as Hasan and Salah 2019
A Blockchain-based Ethereum smart contract scheme can track the provenance and history of digital content to its original source even if it is copied multiple times.
“A scheme using BC-based Ethereum technology, the second largest BC network, specifically Ethereum smart contracts, to track the provenance and history of digital content to its original source, even if it is copied multiple times, is presented in Hasan and Salah (2019).”
From Deepfakes: current and future trends (2024) · cites this paper as Hasan and Salah 2019
Leveraging technologies such as blockchain or privacy-enhancing digital fingerprints can transparently trace content origins.
“Leveraging technologies such as blockchain (Hasan and Salah 2019), or privacy-enhancing digital fi ngerprints can transparently trace content origins (Barrington et al. 2023).”
From Generative AI and deepfakes: a human rights approach to tackling harmful content (2024) · cites this paper as Hasan and Salah 2019
Lip-syncing approaches and image quality metrics with SVM produce very high error rate when applied to detect deepfake videos from the newly produced dataset.
“Other meth- ods such as lip-syncing approaches [67–69] and im- age quality metrics with support vector machine (SVM) [70] produce very high error rate when applied to detect deepfake videos from this newly produced dataset.”
From Deep learning for deepfakes creation and detection: A survey (2022) · cites this paper as [68]
The Related Work section discusses a CNN-based deepfake detector and image-authenticity services, while the Evaluation section concerns the proposed blockchain system. Searches for lip-syncing, image-quality metrics with SVM, and error rates on a newly produced dataset found no passage addressing the claimed comparison. (LLM, conf=0.76)
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Deep Fake Detection Using Computer Vision-Based Deep Neural Network with Pairwise Learning
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Auditable Protocols for Fair Payment and Physical Asset Delivery Based on Smart Contracts
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- Authors
- Hasan, H. R., & Salah, K.
- Affiliations
- Khalifa University of Science and Technology (AE)
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 41596-41606
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2920877282
- Cited by
- 339
- References
- 13
- Citations by year
- 2026: 19 · 2025: 37 · 2024: 54 · 2023: 53 · 2022: 49 · 2021: 55 · 2020: 51 · 2019: 21
- FWCI
- 54.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology in Education and Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- deepfakes, video authenticity, blockchain, smart contracts, Ethereum, IPFS, content provenance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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