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 (dHash), perceptual hash (pHash), and wavelet hash (wHash), which were then compared to the video fingerprints recorded in the blockchain.

Similarity analysis under video manipulation attacks

The robustness of the model was assessed using several attacks applied to videos, including temporal, spatial, geometric, compression, filtering, and adversarial manipulation attacks. This study’s similarity values came from individual experiments for each video manipulation scenario. Each type of manipulation was applied once to create a representative test case, and its similarity scores were recorded for analysis. The goal was to check how well the proposed framework could spot copyright-related changes under various realistic attack conditions. So, the reported values show results from individual test cases, not averages from repeated trials.

Similarity scores using four perceptual hashing algorithms are listed in Table 3. The proposed model achieves a relatively high degree of similarity under most content-preserving attacks, while remaining sensitive to large structural changes in the videos (Table 3).

Performance under temporal transformations

Temporal transformations such as frame insertion, frame deletion, frame substitution, and frame-rate conversion primarily affect the temporal continuity of videos while preserving their spatial features almost intact. Experiments showed that frame insertion and frame substitution achieved more than 80% similarity with all tested fingerprinting algorithms (Figure 4). Therefore, perceptual fingerprints maintained the visual features of videos despite temporal changes.

Frame deletion yielded a slight decrease in similarity values because of the absence of information in the frames. Nevertheless, the fingerprints were similar enough to provide adequate performance for detecting copyright infringement. Frame-rate variations did not significantly affect the similarity measures, as they yielded values greater than 97% in most test samples. This is because frame-rate transformations do not significantly alter frame features.

Influence of spatial and geometric transformations

Geometric and spatial transformations directly affect the visual structures in video frames, making it relatively difficult for the perceptual hashing technique. Locally tampered regions led to only a slight decrease in similarity values, since the visual modifications were localized and did not substantially alter the global perceptual hash.

The cropping operations resulted in higher variation in similarity values due to the removal of parts of the visual material. Rotational transformations caused the greatest reduction in similarity values, with values ranging from about 48% to 66% across perceptual hash techniques. This is understandable since rotation changes the spatial structures that play an important role in the perceptual hash. Scaling the images had little effect on the similarity scores, since the values remained above 96%. Figure 5 summarizes the similarity results obtained under video rotation, including geometric transformations.

Robustness to compression, filtering, and noise

Compression and transcoding are routine activities when storing and distributing multimedia. Experimental analysis has shown that the similarities were consistently above 98% even after MPEG compression, transcoding, and re-encoding with various video codecs. From the findings, it can be inferred that perceptual fingerprints retain most of the visual content despite compression errors and quantization noise. Also, experiments have been conducted on noise addition and filtering. Regardless of how loud the noise was, the similarity was always between 98% and 99%. Additionally, Gaussian, median, and low-pass filtering resulted in minimal changes in similarity, preserving the overall structural information of the video.

Detection of complex and adversarial modifications

The proposed model was also investigated to identify attacks such as adding text or stickers to video frames, merging video clips, and collusion modification attacks. Such a modification is used to preserve consistency while making unauthorized changes to copyrighted material. The outcome of the similarity analysis after adding text to the video frames is shown in Figure 6 below, whereas Figure 7 displays the results of the similarity analysis of merged video clips.

Comparison of the performance of perceptual hashing algorithms

Each of the four perceptual hashing algorithms had specific properties observed during experimental testing. Perceptual hashing (pHash) proved very robust to compression, filtering, and transcoding, while remaining sensitive to structural manipulations. Wavelet hashing (wHash) performed well under local manipulations, such as adding text or objects.

Difference hashing (dHash) could detect gradient-related structural manipulations but was more sensitive to rotation. Average hashing (aHash) was stable when applied to globally applied operations that did not change overall intensity, but was less stable when geometric manipulation occurred. All in all, pHash and wHash produced the best results among the tested algorithms.

Performance evaluation of blockchain technology

Besides content verification performance, the scalability and performance of the blockchain system were also considered. During experiments, it was discovered that the time required for file upload grew linearly with file size and network size, due to the synchronization needed among peers.

Similarly, the time to mine and create blocks grew linearly with the number of peers. This result was predicted because it becomes more difficult to perform transactions as the number of peers grows, requiring more communication. However, the time observed by the authors was realistic enough for the application domain of copyright registration.

The operation of accessing blocks did not show significant variations until the network size exceeded some specific value. The observed performance is also affected by the hybrid storage architecture. In this setup, ownership metadata, transaction information, and perceptual hash values are stored on the blockchain, while CouchDB handles multimedia data. This reduces the amount added to the distributed ledger, helping keep the growth of blockchain storage under control. Even though the authors didn't conduct a specific comparison with a blockchain-only design, experiments show that the system still performs well for transaction processing and retrieval across different network setups. Figures 8–11 summarize the researchers' blockchain performance evaluations across various network configurations.

Overall, the experimental results indicate efficient performance of the suggested system that combines perceptual hashing with blockchain technology. The suggested framework is resistant to content-preserving operations and at the same time sensitive to structure-changing attacks. Additionally, it can register and verify copyrights on the Hyperledger Fabric network.

Data Availability:

The data and codes used in the present study have been uploaded to the Zenodo repository and can be accessed from: https://doi.org/10.5281/zenodo.20665324. These include extracted frame datasets, perceptual hashes, Hamming distance computation, blockchain benchmarking tests, smart contract code, deployment files, and other metadata used in the experiments on copyright registration and verification.

Figure 1: Proposed decentralized video copyright protection framework architecture. The figure shows the overall structure of the proposed framework, including video preprocessing, perceptual hash generation, blockchain registration, ownership verification, and CouchDB storage. Please click here to view a larger version of this figure.

Figure 2: Proposed system model for video copyright protection and verification workflow. The workflow demonstrates video acquisition, frame extraction, perceptual hash generation, blockchain registration, similarity verification, and copyright decision-making processes within the proposed framework. Please click here to view a larger version of this figure.

Figure 3: Sequence diagram for video copyright registration and verification. The sequence diagram depicts the interactions among the content owner, the application layer, the perceptual hashing module, the smart contract, the peer nodes, the ordering service, the blockchain ledger, and CouchDB during copyright registration and verification. Please click here to view a larger version of this figure.

Figure 4: Similarity analysis under multiple frame-editing operations. The figure presents the cumulative similarity performance of the proposed framework across several temporal video manipulation scenarios, including frame insertion, deletion, replacement, and frame-rate modification. Please click here to view a larger version of this figure.

Figure 5: Similarity analysis under video rotation. The figure depicts the impact of video rotation on similarity scores. This shows the effectiveness of the proposed framework even when the videos are rotated moderately. Please click here to view a larger version of this figure.

Figure 6: Adding text to the video. This shows the similarity results after inserting text into the video frames. It demonstrates the proposed framework's ability to identify content changes while verifying ownership. Please click here to view a larger version of this figure.

Figure 7: Merged segments in the video. This represents the analysis of video similarity generated by merging fragments from various sources. Please click here to view a larger version of this figure.

Figure 8: Execution time analysis for uploading files on CouchDB off-chain storage with a varying number of peers. This figure shows the time required by the proposed framework to upload video metadata to off-chain storage for different file sizes and network configurations. Please click here to view a larger version of this figure.

Figure 9: Execution time analysis in seconds for block mining for varying sizes of files with a varying number of peers on the blockchain network. This figure shows the performance of a block mining operation in the Hyperledger Fabric network across different network configurations. This experiment shows the effects of file size and peer-node configuration on system performance. Please click here to view a larger version of this figure.

Figure 10: Execution time analysis in seconds for block creation for varying sizes of files with a varying number of peers on the blockchain network. The figure below shows the time required to create a block in the network under different network configurations. This helps analyze the framework's operational capabilities and scalability. Please click here to view a larger version of this figure.

Figure 11: Execution time analysis in seconds for block access for varying sizes of files with a varying number of peers on the blockchain network. This figure shows the time required to retrieve information from the blockchain system for different file sizes and peer-node configurations. This shows that the proposed framework can be effectively used for copyright validation and ownership retrieval. Please click here to view a larger version of this figure.

S. No.	Author(s)	Application	Blockchain	Security	Protection	Scalability	Copyright Protection	Off-chain storage	Integrity	Decentralized
1	Liu et al.13	Digital copyright protection using blockchain technology	✓	✓	✗	✓	✓	✓	✗	✓
2	Zheng et al.12	Blockchain-based video copyright protection with watermarking	✓	✓	✓	✗	✓	✓	✓	✓
3	Qureshi et al.11	Multimedia copyright management framework	✓	✓	✗	✗	✓	✓	✗	✓
4	Darwish et al.9	Video watermarking and copyright verification	✓	✓	✓	✗	✓	✓	✓	✓
5	Li et al.7	Decentralized music copyright operation management system	✓	✓	✗	✗	✓	✓	✗	✓
6	Pandian et al.10	Blockchain-based video integrity verification	✓	✓	✗	✗	✓	✗	✓	✓
7	Guo J et al.6	Digital rights management for multimedia resources of online education	✓	✗	✗	✗	✓	✓	✗	✓
8	Moolikagedara et al.14	Blockchain-based ownership and traceability system	✓	✓	✗	✗	✓	✓	✗	✓
9	Madapati and Pradhan5	Ethereum-based video copyright protection framework	✓	✓	✗	✓	✓	✓	✓	✓
10	Frattolillo et al.8	Smart contract-enabled digital copyright protection	✓	✓	✗	✓	✓	✓	✗	✓
11	Proposed Framework	Hyperledger Fabric–based video copyright protection using multiple perceptual hashes (aHash, dHash, pHash, and wHash)	✓	✓	✓	✓	✓	✓	✓	✓

Table 1: Comparison of existing blockchain-based copyright protection systems. The table compares current blockchain-based methods of copyright protection with the proposed framework across key factors, including blockchain use, smart contracts, perceptual hashing, scalability, copyright protection, ownership security, integrity checks, and decentralization.

Parameter	Configuration
Blockchain Platform	Hyperledger Fabric 2.4.8
Network Type	Permissioned Blockchain
Number of Organizations	2
Number of Peer Nodes	2
Number of Orderer Nodes	2
Certificate Authorities	2
Communication Channel	autochannel
Smart Contract Name	vitChain
Smart Contract Language	Go
State Database	CouchDB
Deployment Tool	Minifab
Container Platform	Docker
Consensus Mechanism	Raft Ordering Service
Metadata Storage	CouchDB
Ownership Storage	Blockchain Ledger

Table 2: Hyperledger Fabric deployment configuration parameters. The table lists the deployment configuration details of the Hyperledger Fabric blockchain network, including the organization, peers, ordering service, certificate authority, communication channel, consensus mechanism, smart contracts, and storage used for experimentation.

S.No	Video Attack	pHash (%)	wHash (%)	dHash (%)	aHash (%)
1	Frame Insertion Attack	82.81	84.33	81.31	86.42
2	Frame Deletion Attack (space left)	82.69	81.78	80.17	81.07
3	Frame Deletion Attack (space removed)	75.37	78.68	76.19	79.69
4	Frame Replacement Attack	84.86	82.6	84.19	81.07
5	Region Tampering Attack	99.4	98.59	99.46	99.76
6	MPEG Compression Attack	99.64	99.61	99.1	99.64
7	Noise Addition Attack (25%)	99.52	99.58	98.8	99.7
8	Noise Addition Attack (50%)	99.64	99.4	98.68	99.52
9	Noise Addition Attack (75%)	99.52	99.31	98.14	99.22
10	Cropping Attack	69.71	54.81	69.11	57.27
11	Rotation Attack (30°)	54.21	60.61	57.09	65.56
12	Rotation Attack (60°)	48.56	53.97	51.38	57.57
13	Scaling Attack	98.56	99.28	96.81	97.48
14	Translation + Flipping (Left–Right)	52.88	63.46	51.2	68.63
15	Translation + Flipping (Up–Down)	50	45.34	60.7	42.73
16	Median Filtering (25)	98.92	99.1	98.02	99.7
17	Median Filtering (50)	98.08	98.17	97.3	99.28
18	Low-Pass Filtering	97.84	99.13	96.03	96.63
19	Gaussian Blur (σ = 5)	99.76	98.68	98.68	99.76
20	Gaussian Blur (σ = 10)	99.76	97.6	97.78	99.4
21	Re-recording (Camcorder) Attack	98.68	98.83	91.95	93.51
22	Format Conversion (MPEG-4 Encoder)	99.16	99.19	98.14	99.4
23	Format Conversion (H.264 QSV Encoder)	99.76	99.79	99.1	99.82
24	Format Conversion (Libvpx-VP9 Encoder)	99.76	99.7	99.04	99.76
25	Temporal Attack (15 fps)	97.48	98.26	98.02	99.4
26	Temporal Attack (24 fps)	99.16	99.16	98.38	99.82
27	Collusion Attack	99.76	99.28	98.74	99.58
28	Transcoding Attack	99.76	99.7	99.04	99.76
29	Bitrate Change Attack	99.52	99.7	99.4	99.76

Table 3: Similarity analysis between original and modified videos under different attacks. The table illustrates the similarity scores generated by several perceptual hashing techniques under various video manipulations, including frame insertion, deletion, replacement, rotation, compression, scaling, noise addition, text addition, and video merging.

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.

A variety of perceptual hash functions offers additional advantages, enabling detection types not possible with a single perceptual hash function. AHash detects global intensity properties and is less computationally intensive than dHash, which is more focused on local gradient changes and structural variations. On the other hand, pHash uses frequency-domain information and is more robust to compression and filtering operations, while wHash is good at detecting localized changes, such as adding text or stickers. Thus, the combination of all these hash functions will make it highly unlikely to miss detection due to the limitations of each hash function separately. Experimental results show that sensitivity to various types of modifications varies across algorithms, demonstrating the usefulness of applying the multi-hash concept to video manipulation detection.

The use of Hyperledger Fabric and CouchDB reduces the amount of data stored on the blockchain, making metadata management much more efficient. Tests show the framework works well in practice, but we still need to conduct a robust quantitative comparison between hybrid and fully on-chain storage setups. This should be investigated in future studies.

While most other blockchain copyright solutions concentrate only on copyright ownership registration, the introduced approach involves both ownership and content-level validation. Such capabilities can be very useful for resolving copyright issues in practice, as mere ownership records are not sufficient to determine whether an edited or redistributed video is related to the copyrighted material.

Still, some limitations do exist. The test was performed on videos with certain features and under specific conditions. Moreover, geometric manipulations, such as rotation, remain problematic for verifying media using perceptual hashing. In the future, it is expected to test the system on additional multimedia datasets and examine the possibility of making fingerprints rotation-invariant and of using artificial intelligence algorithms to automatically identify copyright infringements.

Audit logging and copyright monitoring were incorporated as architectural components of the proposed framework. However, their effectiveness was not independently evaluated in the present study. Future research will investigate their role in dispute resolution, anomaly detection, and automated copyright monitoring in large-scale multimedia environments.

This study presented a decentralized framework for video copyright protection that integrates Hyperledger Fabric blockchain technology with multiple perceptual hashing techniques to support secure ownership registration and content-based copyright verification. The proposed framework combines aHash, dHash, pHash, and wHash algorithms with smart contract–enabled blockchain services to establish a transparent, tamper-resistant, and verifiable copyright management system.

Experimental evaluation demonstrated that the framework effectively supports copyright verification under a wide range of temporal, spatial, and compression-based video modifications. The use of multiple perceptual hashing techniques improved robustness against common multimedia processing operations while maintaining the ability to detect unauthorized content alterations. In addition, integrating Hyperledger Fabric and CouchDB provided a hybrid storage architecture that preserved data integrity and traceability, while enabling efficient metadata management.

From these findings, it is apparent that the suggested system effectively merges the principles of decentralized management and content-level authentication, thereby overcoming some of the shortcomings of classical centralized copyright protection systems. In addition, the presented system offers transparent auditability, immutable ownership, and verification processes based on content similarity.

In conclusion, the research shows that integrating blockchain and perceptual hashing may offer solutions for next-generation video copyright protection. Future research shall include a more comprehensive evaluation of the performance of the suggested framework on larger, more varied video databases, along with the development of advanced fingerprinting mechanisms.

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.