Today, the Internet is the main source of information for everyone. If we need anything, we simply get it by browsing the data, such as text, audio, video, or images, and everything is available. However, we are missing the originality of the data. In the 1920s, television and video content started gaining popularity. At that time, rebroadcasting programs and films became common, which later raised concerns about copyright protection in 1984. The “Betamax case” addressed the legality of recording content for private use (Rani et al., 2024).

The historical development from 1920 to the present covers the period up to 2025. Broadcasting television and video content has become extremely popular, but the unlawful rebroadcasting of movies and programs remains a significant challenge (Kumar et al., 2021). VSRs were invented between the 1950s and 1970s, enabling the copying of movies and programs. This eventually led to the” Betamax case,” which confronted the legality of recording and broadcasting content. From the 1990s to the 2000s, DVDs and the Internet were used for peer-to-peer file sharing through platforms such as Napster, LimeWire, and BitTorrent (Wu et al., 2022). From 2010 to the present, streaming platforms have grown in popularity. However, unauthorized uploading and distribution of videos have become a major concern. Video uploaders are identified based on their ID’s, but the the copyright regulations remains imperfect. Unauthorized screen recordings and live streaming pose significant challenges. Using such content, individuals often alter the original content due to the availability of video editing software. Blockchain is a decentralized digital ledger that records transactions across several computers while ensuring security, transparency, and immutability (Ma et al., 2018). Each transaction is organized into a block, which is linked to the preceding block by cryptographic hashes, resulting in a chronological “chain” of blocks. Based on its properties, blockchain may provide more security.

However, blockchain provides a safe, transparent, and efficient solution for managing video copyright, empowering creators, combating piracy, and assuring fair pay by using both public and private blockchains. A public blockchain is a decentralized and permissionless network that anyone can access; it is completely open and transparent. It offers global accessibility, allowing anyone with an internet connection to use it (Lee et al., 2021; Garba et al., 2021). The main problems with public blockchains are scalability issues, energy consumption, and privacy concerns. A controlled environment is provided by private blockchains. A private blockchain also faces some issues such as centralization, scalability issues, security risks, high costs, governance difficulties, compliance requirements, privacy concerns, trust issues, and limited innovation. These can be solved through efficient protocols, strong security, cost-saving solutions, privacy tools, and other measures (Guo et al., 2020). A public blockchain is an open, permissionless network that anyone can join to read, write, and validate transactions. It is completely decentralized, with examples including Ethereum and Bitcoin. Blockchain technology provides trust and immutability. In contrast, a private blockchain is a decentralized, permissioned network that is controlled by a single organization or a group of organizations. Only approved users can view or authenticate transactions, providing privacy control. Due to this, it is less decentralized than a public blockchain (Yang et al., 2020; Wang et al., 2023).

Therefore, a perceptual hash (pHash) function is a neat solution to this problem. A perceptual hash function has the characteristic that the hash of an original input is associated with the hash of a slightly altered input. As a result, perceptual hashing can be used to detect manipulated picture and video frames without substantially changing how they look. The distributed hash table (DHT) technique is a well-known feature of the peer-to-peer file sharing network, the InterPlanetary File System (IPFS). A unique 46-byte hash is assigned to every file, including images and videos, to maximize storage and retrieval (Hasan and Salah, 2019). The IPFS hash value is changed when the content creators modifies the image or the video frame in the distributed hash table. Features such as immutability, integrity, dependability, and transaction availability are provided by blockchain technology (Dave et al., 2022). Each block that makes up a blockchain contains a number of transactions (Gürfidan and Ersoy, 2021). Our method involves storing the perceptual hash of every picture or movie shared on the blockchain network’s IPFS distributed file-sharing system (Li and Wang, 2016; Lee and Choi, 2022; Singh and Aggarwal, 2018; Yu et al., 2022; Gangwar et al., 2018).

We propose a solution to the problem of video copyright protection—the use of IPFS-based file sharing and blockchain technology. Blockchain-based solutions have a lot to offer in terms of preventing video copyright violations. We propose a blockchain-based distributed system to prevent copyright violations in videos by utilizing the pHash algorithm. Below is an overview of the contributions made by our study:

Hashing techniques are very effective for storing, retrieving, and managing data. There are four types of hashing techniques related to images: wavelet hashing (wHash), pHash, difference hash (dHash), and average hash (aHash).

With the emergence of blockchain technology, new solutions have arisen to address challenges in digital copyright and Internet business, including video content production. Researchers have argued that conventional means of IP management, including digital watermarking, encryption, and DRM, fall short in providing adequate security and authentication for digital assets. Such centralized systems may be manipulated, pirated, and broadcast without the consent of the creators of such systems.

Blockchain, as designed, provides a shared, decentralized, transparent, and immutable system for operational record-keeping that addresses many of these challenges. Using the concept of blockchain, video content could be registered securely, and the ownership of content could be virtually defensible as it is stored in a decentralized manner. This, in a way, allows creators to assert their ownership of the content, access the path of the material, and prevent piracy. Moreover, the integration of perceptual hashing with blockchain has benefits for security, as video fingerprints can be easily distinguished and stored on the blockchain, decreasing the occurrence of piracy.

The current literature review in Table 1 shows that despite the many opportunities blockchain offers in the area of copyright protection, there are many barriers to its effective implementation. Among these promising directions, challenges such as the workability of blockchain for big data, the problem of integration of various blockchain platforms, and the legal regulation of blockchain in the field of intellectual property management stand out. The majority of established blockchain-based copyright protection systems are still under development and remain unproven in real-world conditions. Several projects are currently exploring how various forms of blockchain can be used for content protection. Some projects focus on improving the video watermarking method, while others combine blockchain with smart contracts when it comes to the automation of licensing and copyright infringement. However, to date, the application of blockchain technology in the management of copyrights remains limited due to technical restrictions such as the rate of transactions, energy consumption, and legal or regulatory constraints. Thus, blockchain-based systems present significant potential for the protection and management of copyrights. However, for a more comprehensive solution, it is crucial to focus on further improving the implementation of blockchain solutions in the management of copyrights. More studies are emerging on how this new technology can complement present copyright frameworks, with the goal of designing a stronger and more effective mechanism to safeguard copyright materials, especially in modern societies where content sharing is rapidly increasing.

The system utilizes the Ethereum blockchain to securely register and verify video ownership. Table 2 lists the software used for the implementation. Video creators upload their content, and Postman is used to interact with backend APIs, which generate a unique perceptual hash of the video. This hash, a digital fingerprint, is then stored on the blockchain to provide an immutable record of ownership. Key frames are extracted from the video for efficient hashing, ensuring minimal computational overhead. When ownership verification is required, perceptual hashes are compared through smart contracts, automating the process of detecting unauthorized use or ownership disputes. If a mismatch occurs, the system flags the content for further review, ensuring transparency and reducing reliance on third-party intervention. The backend, built using Python and Flask, ensures smooth communication with the blockchain, while Postman is used for testing and interaction with APIs. MetaMask is integrated to manage user authentication securely, allowing only authorized users to perform actions such as video registration or claim ownership. Moreover, the Ethereum blockchain ensures that ownership data are decentralized, and hence, secure, and transparent, thereby instilling confidence in users regarding the authenticity of recorded ownership. The approach combines the security of blockchain with the efficiency of perceptual hashing to create a robust video copyright management system that provides enhanced security and transparency in handling digital content for both creators and consumers alike.

In this section, we analyze and discuss the results with respect to our proposed and implemented framework. Figure 3 depicts the result when the proposed smart contract was deployed on the Ethereum blockchain network. It shows smart contract functions such as registeringVideos, getVideo, and videos as inputs to the blockchain network. The orange-colored button indicates functions that write or update state values in the blockchain network. Similarly, the blue-colored button represents functions that read values from the blockchain network. After successful compilation with the Solidity compiler and deployment of the bytecode.

of the smart contract to the blockchain network, we obtained the transaction status, hash, block hash, block number, contract address, owner address, gas values, and bytecode, as shown in Figure 4. Client communication with the video copyright framework is facilitated through the Postman API. Figure 5 illustrates the Postman call made when a video content is registered by a content owner. As a result, it returns the InterPlanetary File System (IPFS) value, the owner’s externally owned account (EOA) address, and the status of the video registration. Figure 6 verifies whether the video content is from the original owner’s address or not. As a result, it shows the IPFS hash value, the owner’s EOA address, and whether the video is registered or not. To compare the copyright of a video, we conducted a simulation using an original video and a modified video. Figure 7 shows the output of the video content comparison, which took the path of the original and modified videos and compared their frames using their pHash values. As a result we get the video as original video or copyrighted video.

Ganache is a private network used on the Ethereum platform for testing smart contracts locally. Figures 8–10 show the Ganache accounts and transactions within the Ethereum network. Figure 8 represents the EOA address, Ether account balance, transaction details, and index value. Figure 9 shows the transaction hash value, the sender’s address, the contact address, and the amount of gas used for the deployed contract. Figure 10 shows the number of blocks, a timestamp value with date and time, and the amount of gas used for automatically initiated transactions.

To analyze the framework’s performance at increased transactions and to measure its efficiency, tests were conducted on various important functionalities of the model using an EVM node. Operations such as video registration and verification were thoroughly tested at various transaction rates, and the results were noted for future reference. The same simulation was run five more times to find variations, and the average results were taken into consideration. The results are shown in Tables 3, 4. All values are rounded to two decimal places. For performance testing using Hyperledger Caliper, we conducted 5,000 transactions, with each transaction consisting of three operations: RegisterVideo, GetVideo, and CompareVideo. Each operation was tested at transaction rates of 1,000, 2,500, and 4,000 transactions per second.

These functions exhibited consistent latency between 4.07 and 5.27 s at different transaction send rates (1,000, 2,500, and 4000TPS) for the 5,000 transactions submitted to an EVM node. Latency represents the interval between a transaction’s start and network confirmation. Figure 11 depicts that the RegisterVideo function takes more latency compared to the other subfunctions such as Get Video and Compare Video as RegisterVideo deals with hash values and IPFS values for video ownership. GetVideo is a kind of read transaction. Therefore, it has the lowest latency, that is, 4.4 s compared to the other functions and subfunctions. The Compare Video function compares the original video with the edited video/copyright-violated videos by analyzing perceptual hash and IPFS values. Therefore, Compare Video has a latency of approximately 4.85 s.

Furthermore, the number of transactions that are handled by a network per unit time is throughput, and the throughput analysis also demonstrated consistent behavior, with 409 to 502 transactions per second for each function. GetVideo had the highest throughput, while RegisterVideo had the lowest at the same transaction end rates. Figure 12 reveals that the read transaction, i.e., GetVideo, achieves higher throughput compared to the other functions because this function verifies the video using its IPFS hash value and the address of the uploader. Registe Video had the minimum throughput of 409.87 s due to its functionality. The Compare Video function compares the original video with the edited video by analyzing perceptual hash and IPFS hash values. Therefore, Compare Video takes 477.23 s more. The throughput graph in Figure 12 reveals that the framework performs better under higher loads.

In addition, CPU, memory, and traffic resource usage data were computed and examined. The parameters were examined during the same 5,000 transactions, under transaction input rates of 1,000, 2,500, and 4,000 TPS. The highest CPU usage of the framework was 81.16%, and the average CPU usage was 35.92%, with a highest average range of 54.05%, as illustrated in Table 4. CPU usage for each function is shown in Figure 13. Memory consumption across all tested functions averaged between 672 MB and 775 MB, with the highest memory usage of 1,020 MB observed for the GetVideo function, as shown in Figure 14. Based on traffic data, RegisterVideo registered the highest average traffic at 30.5 MB, whereas CompareVideo registered the lowest average traffic at 28.7 MB. For data traffic, CompareVideo’s minimum was 12 MB and RegisterVideo’s maximum was 20.8 MB. Figure 15 displays the traffic data results. From the analysis results, it is evident that the proposed framework guarantees stable performance in stress testing, which can be employed in scalable, efficient, and reliable video services.

This approach achieves a balanced combination of robustness, resource efficiency, and scalability for video copyright protection.

The study successfully demonstrates that a fully decentralized, blockchain-based video copyright protection system is both feasible and effective. This can be achieved using blockchain: when video creators securely register on the blockchain and owners verify their ownership of any piece of content through this mechanism, any alteration or piracy can be easily identified. This study validates the performance of a reliable and efficient system, featuring video registration, verification, and comparison modules that run as designed. It provides a transparent and tamper-proof platform for the open and secure management of copyrights, an essential aspect of copyright protection and intellectual property management.

In the future, additional advanced methods of video analysis can be added to make a more accurate comparison of the contents. In addition, incorporating machine learning algorithms could enable more efficient detection of subtle modifications or plagiarized content. Expanding the platform to other forms of digital media will further increase the number of potential use cases for this blockchain platform. Building an easy-to-use front-end interface will greatly extend the target group and, by that means, foster further diffusion. Deploying the system on a public blockchain network will increase the system’s scalability manyfold, with real-world testing across various scenarios. These improvements could solidify the system’s role in protecting intellectual property rights in the evolving digital landscape.