A Decentralized Approach to Video Copyright Protection Using Private Blockchain and Perceptual Hashing

Summary

This study presents a decentralized framework for video copyright protection that integrates private blockchain, smart contracts, and perceptual hashing techniques. The aim is to enable secure ownership registration, immutable record management, and efficient similarity-based verification of video content in distributed environments.

Abstract

The growing popularity of video content sharing on the Internet has raised concerns about copyright violations, piracy, and ownership disputes. Traditional centralized methods for handling copyright face transparency issues, susceptibility to manipulation, and reliance on trusted third parties. Therefore, this study presents a decentralized video copyright protection system based on Hyperledger Fabric, smart contracts, and perceptual hashing. Firstly, the frames from the videos are extracted, and perceptual fingerprints are created using the average hash (aHash), difference hash (dHash), perceptual hash (pHash), and wavelet hash (wHash) algorithms. Hashes and ownership information are stored on the blockchain, while multimedia assets are kept outside the ledger. Copyright checks are performed using frame-level similarity comparisons based on the Hamming distance. The proposed framework was tested across various temporal, spatial, and compressive manipulations, including frame insertion, deletion, cropping, resizing, rotation, filtering, and transcoding. The experiments showed high robustness to such changes while simultaneously detecting unauthorized modifications. Therefore, this technology can provide a practical solution to copyright issues using a decentralized blockchain framework.

Introduction

Advancements in digital multimedia technologies have changed the way videos are produced, distributed, and consumed. Video streaming, social networks, and Internet-based content-sharing systems enable the quick and easy distribution of videos to audiences worldwide. While technological advancements allow more creative possibilities in the production of digital video content and the growth of online business, these same advancements have made it easier for content creators to experience copyright violations, piracy, and problems with ownership¹. Videos can easily be copied, edited, re-encoded, and redistributed, making protecting intellectual property increasingly difficult in today's multimedia world^2,3.

Typically, the current copyright management systems depend on a central authority to register content, validate ownership, manage licensing agreements, and handle disputes. Despite being widely adopted, these management systems are prone to several significant limitations⁴. First, centralization exposes copyright management systems to potential cyberattacks, data manipulation, and downtime. Additionally, copyright ownership is stored by trusted third parties, which may not be entirely desirable in terms of auditing and maintaining long-term reliability of such information⁵. An increased amount of digital multimedia content makes conventional verification processes less efficient, as they tend to take too much time and become impractical⁶.

The technology of blockchain appears to be one of the solutions that can resolve trust and transparency issues within the distributed environment⁷. Using immutable ledgers via cryptography and consensus mechanisms, blockchain enables secure recordkeeping without a central authority controlling the process. As soon as the records are added to the blockchain, they become immutable and unchangeable, thus providing higher levels of traceability and accountability⁸. That is why many efforts have been made to study blockchain applications in areas such as DRM, IP, and multimedia ownership assurance⁹. Among existing blockchain platforms, Hyperledger Fabric has received significant attention for its features, including a permissioned architecture, a modular structure, support for smart contracts, privacy, and fast processing. In contrast to public chains, Hyperledger Fabric's permissioned architecture makes it ideal for copyright protection systems¹⁰.

However, there are certain limitations to using a blockchain as the sole means of identifying the copyright status of multimedia content¹¹. First, traditional cryptographic hash functions (for example, SHA-256) produce completely different hash results even when minor changes are made to a video¹². Although it is a desirable characteristic for cryptographic purposes, it hinders visual recognition of similar or modified versions of copyrighted multimedia content. Thus, multimedia copyright verification must be based on content-aware algorithms that can detect perceptual similarities in images or videos¹³.

One of the most promising ways to address the copyright problem in multimedia content is to use perceptual hashing¹⁴. As opposed to cryptographic hashing, perceptual hashing produces fingerprints that encode visual properties of the image or video frame¹⁵. Thus, even if the file has been changed through the usual processes (compression, filtering, rescaling, adding noise, and so on), the hash remains relatively unchanged¹⁶. There are four types of perceptual hashes currently in use: average hash, difference hash, perceptual hash, and wavelet hash^17,18.

There have been numerous research studies investigating the use of blockchain technology in conjunction with copyright protection techniques for multimedia data¹⁹. Previous works have utilized blockchain technology for ownership registration, digital watermarking, distributed storage and management of evidence, decentralized rights management, and secure storage of multimedia content²⁰. The Hyperledger Fabric has been leveraged in previous work to enable copyright registration, rights management, and transaction auditing via smart contracts and permissioned consensus protocols^21,22. Likewise, perceptual hashing and watermarking approaches have been proposed to detect duplicate and manipulated multimedia content. Despite these promising works in enabling copyright management using blockchain technology, a few gaps still exist^23,24. Firstly, many previous frameworks are primarily concerned with ownership registration but lack the capability to verify content. Secondly, many works have relied on a single hashing or watermarking approach, thereby making them more vulnerable to video manipulation attacks^25,26. Table 1 shows that existing systems generally support ownership registration but rarely combine decentralized ownership management with multi-hash content verification.

With advances in video editing technology, there is a greater need for efficient video ownership verification to prevent copyright infringement²⁷. Current technology offers several methods to temporally manipulate videos by adding, removing, replacing, and changing the frame rate. Similarly, spatial manipulation options include frame resizing, scaling, filtering, compression, transcoding, and geometric alterations of videos²⁸. These manipulations make it difficult to identify copyright owners, even though they do not change the video's appearance. An efficient system needs to be able to differentiate between permissible manipulation, ownership, and copyright infringement with integrity²⁹.

To address the challenge of efficient video copyright management, this paper proposes a decentralized model that leverages Hyperledger Fabric, smart contracts, and perceptual hashing. The proposed framework will generate perceptual fingerprints using hashing techniques, extracting video frames^30,31. Four different hashing techniques, namely aHash, dHash, pHash, and wHash, will be employed to generate perceptual fingerprints. Ownership information, timestamp, and hashes are recorded on the permissioned blockchain-based ledger. To improve scalability, CouchDB is used in conjunction with blockchain to store multimedia content off-chain.

The proposed framework employs Hamming distance analysis to validate frame-level similarity and detect modifications, duplications, or illegal use of videos. By combining several perceptual hashing techniques with blockchain-based audibility, the solution achieves both content-level authentication and tamper-proof ownership management. Unlike previous systems, which rely on a single verification method, the proposed framework uses multiple perceptual descriptors to withstand a range of temporal, spatial, and compressive video manipulations.

These are the primary contributions that are presented in the research work: (1) A decentralization approach for the secure copyright registration and verification process for videos using the Hyperledger Fabric framework; (2) Multiple perceptual hashing algorithms, such as aHash, dHash, pHash, and wHash, are used to create multiple video fingerprints that can recognize similar videos even after being edited; (3) A distributed database model using both Hyperledger Fabric and CouchDB is developed to address scalability issues without compromising data integrity and traceability; (4) A comprehensive verification process involving the calculation of Hamming distance is formulated to determine the manipulation of videos with respect to frames and pixels; (5) Experiments are conducted to show the capability of the proposed method in detecting modified videos.

Thus, by integrating blockchain-based ownership and multiple hashing algorithms, the proposed framework offers a feasible approach to decentralized video copyright protection.

Protocol

This study did not involve human participants, human subjects, animal subjects, or clinical specimens. Therefore, institutional ethics approval and informed consent were not required.

Materials and software 

The environment used to deploy the framework was Hyperledger Fabric version 2.4.8, CouchDB, Docker Containers, Minifab deployment, FFmpeg multimedia processing applications, and the Go programming language. Hyperledger Fabric provided services for decentralized ledger management and smart contract execution, while CouchDB stored and retrieved copyright information efficiently via off-chain storage. FFmpeg was used for preprocessing videos and extracting video frames, while perceptual hashing algorithms were applied through image hashing libraries in the Go programming language. A list of all tools and their respective descriptions is illustrated in the Table of Materials.

Framework architecture

The proposed framework employs perceptual hashing algorithms within the permissioned blockchain Hyperledger Fabric to enable a decentralized approach to video copyright protection and ownership validation. The framework consists of five main components: video preprocessing, perceptual hash generation, blockchain-based copyright registration, and similarity-based verification. In this framework, Hyperledger Fabric serves as the trust layer, providing immutable ownership records, while CouchDB provides off-chain storage. The overall architecture of the proposed framework is depicted in Figure 1.

Model of the system for copyright verification

A detailed workflow of the system under investigation is shown in Figure 2. It starts with video acquisition, followed by frame extraction at regular intervals using FFmpeg. The extracted frames are processed within the Hyperledger Fabric implementation environment, which includes actions such as ownership registration, certificate administration, peer communication, and transaction validation. Perceptual hashing is applied to the extracted frames, followed by Hamming distance calculation to assess the degree of similarity. If the Hamming distance is below the threshold, the frame is considered authentic and similar to the registered content; otherwise, it is treated as modified content.

Video acquisition, preprocessing, and perceptual hashing creation

Video samples used in the experiment were MP4 files at 480p resolution, averaging about 26 seconds in length. The evaluation dataset had 15 video samples, including an original reference video and several modified versions. These modifications included common editing tasks such as changing frame rates, inserting, deleting, or substituting frames, and applying distortions such as cropping, scaling, rotation, and overlay effects. The dataset also included compression, noise, text, and sticker additions, glitch effects, and more. All the videos were in MP4 format at 480p resolution and underwent the same preprocessing and fingerprint generation steps. Designed to test the system's resilience, this dataset covers a wide range of real-world scenarios involving infringement and manipulation. Before being subject to copyright detection and confirmation, video files underwent preprocessing in FFmpeg. Frames from video files were extracted at 1-second intervals to obtain condensed content summaries in the form of visual content that remained computationally inexpensive to process. Next, these frames were converted to grayscale and normalized to minimize changes in pixel intensities arising from lighting conditions, encoding, and compression differences.

Four perceptual hashing functions were used to produce video content fingerprints in the form of average hashing (aHash), difference hashing (dHash), perceptual hashing (pHash), and wavelet hashing (wHash).

For hash generation, frames were first converted to grayscale and resized to 8x8 pixels to compute aHash and dHash. With aHash, a 64-bit fingerprint was generated by comparing each pixel to the average grayscale value. The dHash algorithm did something similar, creating a 64-bit fingerprint too, but it looked at how adjacent horizontal pixel intensities differed. For pHash, frames got resized to 32x32 pixels, then the discrete cosine transform was computed. To generate the 64-bit perceptual fingerprint, only the low-frequency 8x8 DCT coefficients were used. As for wHash, frames were again resized to 32x32 pixels, but this time were processed with a Haar wavelet transform. A 64-bit hash was generated from the lowest-frequency wavelet coefficients extracted. All these parameter settings aim for a good balance between computational efficiency and strength to handle typical video tweaks.

To perform efficient hash function computations, these images were normalized and resized. The average hashing method works by comparing pixel intensities to those of the mean of the picture. Difference hashing operates similarly but focuses on local gradients by comparing pixel intensities with those of neighboring pixels. Perceptual hashing uses Fourier features that result from DCT, while wavelet hashing relies on wavelet transform results to extract features.

Blockchain-based copyright registration and verification

The perceptual fingerprints, ownership metadata, timestamps, and transaction details obtained are fed into the Hyperledger Fabric blockchain via the smart contract, vitChain. Blockchain implementation includes two organizations, two peers, two ordered nodes, two certificate authority (CA) nodes, and CouchDB state databases, all linked via an auto channel. The permissioned blockchain framework facilitates secure processing, restricted access, and tamper-proof storage of copyright details.

The copyright registration procedure commences once the content owner uploads the video via the user interface. When a user submits a video for copyright registration, the application layer calls the vitChain smart contract via the Hyperledger Fabric SDK. This registration includes a unique transaction ID, owner info, video ID, time stamp, and four types of perceptual hash values: aHash, dHash, pHash, and wHash. Also, there’s metadata about the content. The app sends the transaction proposal to the endorsing peers. These peers then run the smart contract logic and ensure the transaction complies with the endorsement policy. Upon successful validation, the transaction moves to the ordering service. Here, verified transactions are grouped into blocks and sent to peer nodes for inclusion in the ledger. Meanwhile, the related metadata goes into CouchDB. They connect to the blockchain's records via the transaction ID, enabling easy lookups and keeping both on-chain and off-chain data consistent.

Following the preprocessing and perceptual hash computations, the fingerprints, along with their associated metadata, are assembled into a transaction to be executed by the smart contract. Transaction validation is performed by participating peers in accordance with the specified endorsement policy. Upon endorsement, the transaction is passed to the ordering service for inclusion in a block on the distributed ledger. For reliable operation, we included basic error-handling in the registration and verification processes. Before a transaction happens, the app checks mandatory metadata, ownership info, and perceptual hashes to avoid issues. If transactions don’t meet the endorsement rules or have invalid parameters, peer nodes reject them outright. When there are problems such as communication failures or processing errors, the system logs them and sends an admin alert. This helps prevent minor processing errors from propagating through the system. During copyright checks, videos containing invalid metadata or corrupted hash values are excluded from the verification process. Videos for which fingerprint generation fails are similarly excluded from the verification process. Overall, these steps keep the data clean and strengthen the framework’s reliability. The ownership information for the copyrighted video becomes immutable and is available for later retrieval.

Verification

The verification process involves similar preprocessing and fingerprint-computation steps, followed by fingerprint comparison against stored fingerprints in the blockchain using a Hamming-distance-based similarity measure. Based on the similarity measure, the smart contract determines the copyright information of the submitted video. The interaction workflow is depicted in Figure 3.

Similarity-based copyright verification

The copyright verification process compares perceptual fingerprints derived from the query video with fingerprints previously stored in the blockchain system. The same preprocessing and hash-value generation process used during registration is repeated in the verification phase to maintain consistency. Similarity is measured using the Hamming distance, which counts the number of bit differences between two hash values. Smaller Hamming distances indicate greater similarity between the two videos, while higher Hamming distances indicate content manipulation. To see if the query video matched the registered video, we used the Hamming distance between their perceptual hash values. Based on preliminary experiments, a Hamming distance threshold of 20 was selected to provide a suitable balance between robustness to common video-processing operations and sensitivity to unauthorized modifications. Videos with a Hamming distance of 20 or less were considered to represent the same copyrighted content. If it were more, it would show big changes. This 20 was chosen to handle common video edits like compression, filtering, transcoding, and frame rate changes, but it still picks up unauthorized changes. Measurements of similarity are made for all collected frame fingerprints, then aggregated to produce a copyright verification conclusion. Videos that deviate from the registered content are flagged for additional scrutiny.

Deployment environment

The testbed environment was created using Docker containers and the Minifab orchestration tool. A blockchain network based on Hyperledger Fabric version 2.4.8 was deployed with CouchDB as the state database and storage layer for smart contract data. The vitChain smart contract was implemented in Go and deployed using the Hyperledger Fabric chaincode lifecycle. The deployment parameters are summarized in Table 2. The network consisted of two participating organizations, namely the Content Creator and the Videographer. To ensure transaction integrity and prevent unauthorized copyright registration or modification, a multi-organization endorsement policy was implemented, requiring approval from both organizations before a transaction could be committed to the ledger. Specifically, the endorsement policy followed an AND rule, expressed as AND ('ContentCreatorMSP.peer','VideographerMSP.peer').

Results

To assess the efficacy of the proposed decentralized copyright protection model, it is tested against various video manipulation attacks that frequently occur during multimedia transmission, editing, and copyright infringement. Two major factors in the assessment include: (i) the reliability of the perceptual hashing algorithm in detecting copyright and tampering, and (ii) the performance of the Hyperledger Fabric blockchain network. Similarity scores were generated by calculating average hash (aHash), difference hash (d...

Discussion

As the proposed framework demonstrates, integrating blockchain with perceptual hashing can effectively protect videos from copyright infringement in a decentralized manner. First, by combining immutable ownership registration and perceptual hashing, it satisfies both the primary conditions of copyright management systems: secure ownership registration and identification of copyrighted materials. Second, using Hyperledger Fabric enables access control, transparent transaction handling, and tamper-proof record management.<...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
CouchDB	Apache Software Foundation	https://couchdb.apache.org	NoSQL database used for off-chain storage of video metadata and multimedia content.
Docker	Docker Inc.	https://www.docker.com	Containerization platform used to deploy blockchain nodes, CouchDB instances, and supporting services.
FFmpeg	FFmpeg Developers	https://ffmpeg.org	Multimedia processing framework used for video decoding, frame extraction, and preprocessing.
Go Programming Language	Google LLC	Version 1.20+; https://go.dev	Programming language used to develop the vitChain smart contract (chaincode).
Google Drive	Google LLC	https://drive.google.com	Storage platform used for hosting and retrieving experimental video datasets.
Hyperledger Fabric	Hyperledger Foundation	Version 2.4.8; https://hyperledger-fabric.readthedocs.io	Permissioned blockchain platform used for decentralized copyright registration, transaction validation, and ledger management.
ImageHash Library	Loupax	https://github.com/loupax/imagehash	Library used to generate perceptual hash values for video-frame authentication.
Minifab	IBM / Hyperledger Community	https://github.com/hyperledger-labs/minifab	Lightweight deployment and orchestration tool used to configure and manage the Hyperledger Fabric network.
Ubuntu Linux	Canonical Ltd.	Ubuntu 22.04 LTS	Operating system used for blockchain deployment and experimental evaluation.
Visual Studio Code	Microsoft Corporation	https://code.visualstudio.com	Integrated development environment used for source code development and debugging.