With the rapid advancement of artificial intelligence and video editing technologies, it has become increasingly easy to manipulate digital videos without leaving visible traces. This creates serious challenges in domains such as journalism, legal investigations, surveillance systems, and digital forensics, where video evidence must be trusted. Existing video verification approaches mainly rely on cryptographic signatures and blockchain storage to detect tampering and maintain an immutable record of video data. However, many current systems verify videos only at the file level and may not provide fine-grained detection of frame-level modifications. This paper proposes Verixa, a secure video proofing architecture designed to ensure the authenticity and integrity of digital videos from the moment of capture. In the proposed system, the recorded video is first divided into frames, and each frame is processed as pixel data to generate a cryptographic hash using the SHA-256 algorithm. These frame hashes are then organized into a Merkle tree, producing a single root hash that represents the entire video sequence. The root hash is digitally signed using the Elliptic Curve Digital Signature Algorithm (ECDSA) within secure hardware environments such as Trusted Platform Modules (TPM) or Trusted Execution Environments (TEE). The generated proof, along with metadata such as timestamp and device ID, is stored on a blockchain ledger, while the actual video is stored in cloud storage. During verification, the video is reprocessed to reconstruct the Merkle tree and validate the signature against the blockchain record. If the values match, the video is confirmed as authentic. The proposed approach

The authenticity and integrity of digital video evidence have emerged as critical challenges in contemporary surveillance and forensic investigations. Given the rapid advancement of video editing technologies and artificial intelligence-based media manipulation techniques, ensuring the reliability of digital recordings has gained increasing importance. This paper presents Verixa, a secure video integrity verification framework developed to protect digital evidence from tampering and unauthorized modification. The proposed system employs cryptographic hashing to generate unique digital fingerprints for video frames and organizes these hashes through a Merkle-tree structure to facilitate efficient integrity verification. Additionally, secure hardware modules such as Trusted Platform Modules (TPM) or Trusted Execution Environments (TEE) are utilized to safeguard private cryptographic keys and enable trusted digital signatures. The proposed architecture enhances the reliability of video authentication by providing rapid verification, scalable data handling, and robust tamper detection capabilities. The integration of cryptographic security techniques with structured data verification offers a promising solution for ensuring the authenticity and reliability of digital video recordings.

[1]L. Lawrence and S. R. Shreelekshmi, “VECDSigL: Video integrity verification using elliptic curve digital signature links,” Software Impacts, vol. 15, p. 100474, 2023. [2]S. Mercan, M. Cebe, R. A. Aygun, K. Akkaya, and E. Toussaint, “Blockchain-based video forensics and integrity verification framework for wireless Internet-of-Things devices,” Marquette University, 2021. [3]S. M. Darwish, M. M. Abu-Deif, and S. M. Elkafas, “Blockchain for video watermarking: An enhanced copyright protection approach for video forensics based on perceptual hash function,” PLOS ONE, 2024. [4]H. H. Pajouh, M. A. Rashid, F. Alam, and S. Demidenko, “IoT big data provenance scheme using blockchain on Hadoop ecosystem,” Journal of Big Data, 2021. [5]Y. Li, X. Yang, P. Sun, H. Qi, and S. Lyu, “Celeb-DF: A large-scale challenging dataset for deepfake forensics,” Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2020. [6]S. Verdoliva, P. Bestagini, M. Tagliasacchi, and S. Tubaro, “FOCAL: A forgery localization framework based on video coding self-consistency,” arXiv preprint arXiv:2008.10454, 2020. [7]W. A. Mahrous, M. Farouk, and S. M. Darwish, “An enhanced blockchain-based IoT digital forensics architecture using fuzzy hash,” IEEE Access, vol. 9, pp. 151327–151342, 2021. [8]S. Blake, “Embedded blockchains: A synthesis of blockchains, spread spectrum watermarking, perceptual hashing and digital signatures,” arXiv preprint, 2024. [9]S. L. Madapati and N. H. Pradhan, “Decentralizing video copyright protection: A novel blockchain-enabled framework with performance evaluation,” Frontiers in Artificial Intelligence, 2025. [10] T. B. Ogunseyi and O. M. Adeyodo, “Cryptographic techniques for data privacy in digital forensics,” IEEE Access, vol. 11, 2023. [11]J. Ceron, C. Tinupucla, and P. Shiguihara, “A survey of blockchain for video integrity,” Engineering Proceedings, vol. 42, 2023. [12]A. Heidari, N. J. Navimipour, H. Dag, S. Talebi, and M. Unal, “A novel blockchain-based deepfake detection method using federated and deep learning models,” Cognitive Computation, 2024. [13]A. Prathap and B. M. Beena, “Blockchain based deep fake detection and verification,” Journal of Electrical Systems, 2025. [14]A. Rejeb, J. G. Keogh, and H. Treiblmaier, “Leveraging the Internet of Things and blockchain technology in supply chain management,” Future Internet, vol. 11, no. 7, 2019. D. Danko, S. Mercan, M. Cebe, and K. Akkaya, “Assuring the integrity of videos from wireless-based IoT devices using blockchain,” Proc. IEEE MASS Workshops,2019

© 2025 International Journal of Engineering and Techniques (IJET).

Digital Object Identifier (DOI)

IJET assigns a unique DOI to every accepted article via Zenodo for persistent linking and citation. Your article’s DOI: https://doi.org/{{doi}}

Open DOI About Zenodo DOI

Close