Copyright Information Research Articles

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Adaptive reversible image-data hiding algorithm based on blockchain and zero watermarking

An adaptive reversible image-data hiding (ARIdH) algorithm is proposed. First, the variance of the secret plain image is calculated and taken as feature values, by a new integer extraction model (NewIEM). Then, a new key transform model (NewKTM) is constructed and all feature values are converted into initial values. Second, discrete wavelet transform (DWT) is applied to the secret plain image, and a new adaptive threshold sparse model (ATSM) is designed to sparsify the DWT coefficients. Third, the sparse matrix is compressed and measured by a measurement matrix produced by NewNM to obtain a middle cipher image (MCI). Then, MCI is divided into blocks, and executed by both XOR diffusion between blocks and spiral scanning diffusion within blocks, to generate a new cipher image (NCI). After that, a carrier image is confused by random sequence and NCI is embedded into it by matrix encoding technique. To realize copyright authentication, an authentication framework based on blockchain and zero watermarking is designed. Specifically, perform an XOR operation between the feature matrix of NCI and the QR code containing copyright information to generate a zero watermarking. Then, smart contracts are designed to store the data and used for authentication.

Global Copyright Consortium: Empowering Decentralisation with Authority - Backed Blockchain

Abstract: Digital images are transferred with ease through the network. Many users are using the images without the knowledge of the owners. Zero watermarking does not alter the original information contained in vector map data and provides perfect imperceptibility. The use of zero watermarking for data copyright protection has become a significant trend in digital watermarking research. However, zero watermarking encounters tremendous obstacles to its development and application because of its requirement to store copyright information with a third party and its difficulty in confirming copyright ownership. However, traditional digital image generation methods have high operational requirements for designers due to difficulties in collecting data sets and simulating environmental scenes, which results in poor quality, lack of diversity, and long generation speed of generated images, making it difficult to meet the current needs of image generation.

A Blockchain-Based Privacy Preserving Intellectual Property Authentication Method

With the continuous advancement of information technology, a growing number of works, including articles, paintings, and music, are being digitized. Digital content can be swiftly shared and disseminated via the Internet. However, it is also vulnerable to malicious plagiarism, which can seriously infringe upon the rights of creators and dampen their enthusiasm. To protect creators’ rights and interests, a sophisticated method is necessary to authenticate digital intellectual property rights. Traditional authentication methods rely on centralized, trustworthy organizations that are susceptible to single points of failure. Additionally, these methods are prone to network attacks that can lead to data loss, tampering, or leakage. Moreover, the circulation of copyright information often lacks transparency and traceability in traditional systems, which leads to information asymmetry and prevents creators from controlling the use and protection of their personal information during the authentication process. Blockchain technology, with its decentralized, tamper-proof, and traceable attributes, addresses these issues perfectly. In blockchain technology, each node is a peer, ensuring the symmetry of information. However, the transparent feature of blockchains can lead to the leakage of user privacy data. Therefore, this study designs and implements an Ethereum blockchain-based intellectual property authentication scheme with privacy protection. Firstly, we propose a method that combines elliptic curve cryptography (ECC) encryption with digital signatures to achieve selective encryption of user personal information. Subsequently, an authentication algorithm based on Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK) is adopted to complete the authentication of intellectual property ownership while encrypting personal privacy data. Finally, we adopt the InterPlanetary File System (IPFS) to store large files, solving the problem of blockchain storage space limitations.

An invisible, robust copyright protection method for DNN-generated content

Wide deployment of deep neural networks (DNNs) based applications (e.g., style transfer, cartoonish), stimulating the need for copyright protection of such application’s production. Though some traditional visible copyright techniques exist, they often introduce undesired artifacts and compromise the aesthetic quality of the images. In this paper, we propose a novel invisible, robust copyright protection method, which is composed of two networks: the copyright encoder and the copyright decoder. The former projects the copyright information to the invisible perturbation with the drive of both the input of images and copyright information, thereby adding it to the image and yielding encoded images. The copyright decoder extracts copyright information from encoded images. Moreover, a robustness module is integrated to enhance the decoder’s ability to decipher images against various distortions encountered on social media platforms. Furthermore, the loss function is elaborately designed, taking into account both feature space and color space, to guarantee the quality of encoded and decoded copyright images. Extensively objective and subjective experiments validate the effectiveness of the proposed method. Additionally, the physical test is conducted by posting the encoded images to social media (e.g., Weibo and Twitter) and downloading them to verify the feasibility of the proposed method in practice.

Image Copyright Protection Based on Blockchain Technology Review

On a daily basis, a significant number of individuals distribute several photos and videos that have been marginally modified from the original material produced by copyright owners, such as photographers, graphic designers, and video producers. Individuals that infringe upon the rights of others, lacking the legal authority to access multimedia content, employ various digital image and picture manipulation techniques, it involves converting to gray scale, trimming, rotating, contracting the frame, and adjusting the background speed, to modify said content. Blockchain technology obviates the necessity of an intermediary, hence circumventing the possibility of a singular point of failure. Infractions to copyright poses a significant barrier to protecting commercial image and video information. The IPFS blockchain technology offers on-chain preservation for copyright information and off-chain storing for distinct multimedia files. The enhanced perceptual hashing algorithm significantly enhances the precision of identifying connections to identify digital image piracy. The photographers and designers that submit their photographs on websites are experiencing significant dissatisfaction due to a prevalent practice in which others attempt to claim credit and profit from the initial creator's effort.

Ambiguity attack against text-to-image diffusion model watermarking

In recent years, the text-to-image diffusion models have achieved excellent performance. Among them, stable diffusion models (SDMs) have become one of the most widely used models because of their excellent performance. Scholars have proposed many model watermarking techniques to protect the copyright of the text-to-image diffusion models. In order to measure the security and potential risks of the existing text-to-image diffusion model watermarking techniques, an ambiguity attack against the text-to-image diffusion model watermarking is proposed for the first time in this paper. Specifically, we take the SDMs as an example, take advantage of the reversibility of the model watermarking and combine the ideas of adversarial examples and discrete prompt optimization to re-embed a forged watermark in the watermarked SDMs, thus confounding the watermark containing copyright information. A large number of experiments show that our ambiguity attack is effective and can make the original watermark lose its uniqueness without changing the watermarked text-to-image diffusion models.

A Self-Defense Copyright Protection Scheme for NFT Image Art Based on Information Embedding

Non-convertible tokens (NFTs) have become a fundamental part of the metaverse ecosystem due to its uniqueness and immutability. However, existing copyright protection schemes of NFT image art relied on the NFTs itself minted by third-party platforms. A minted NFT image art only tracks and verifies the entire transaction process, but the legitimacy of the source and ownership of its mapped digital image art cannot be determined. The original author or authorized publisher lack an active defense mechanism to prove ownership of the digital image art mapped by the unauthorized NFT. Therefore, we propose a self-defense copyright protection scheme for NFT image art based on information embedding in this paper, called SDCP-IE. The original author or authorized publisher can embed the copyright information into the published digital image art without damaging its visual effect in advance. Different from the existing information embedding works, the proposed SDCP-IE can generally enhance the invisibility of copyright information with different embedding capacity. Furthermore, considering the scenario of copyright information being discovered or even destroyed by unauthorized parties, the designed SDCP-IE can efficiently generate enhanced digital image art to improve the security performance of embedded image, thus resisting the detection of multiple known and unknown detection models simultaneously. The experimental results have also shown that the PSNR values of enhanced embedded image are all over 57db on three datasets BOSSBase, BOWS2 and ALASKA#2. Moreover, compared with existing information embedding works, the enhanced embedded images generated by SDCP-IE reaches the best transferability performance on the advanced CNN-based detection models. When the target detector is the pre-trained SRNet at 0.4bpp, the test error rate of SDCP-IE at 0.4bpp on the evaluated detection model YeNet reaches 53.38%, which is 4.92%, 28.62% and 7.05% higher than that of the UTGAN, SPS-ENH and Xie-Model, respectively.

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RAWIW: RAW Image Watermarking robust to ISP pipeline

Invisible image watermarking is essential for image copyright protection. Compared to RGB images, RAW format images use a higher dynamic range to capture the radiometric characteristics of the camera sensor, providing greater flexibility in post-processing and retouching. RAW images are considered the original format for distribution and image production, thus requiring copyright protection. Existing watermarking methods typically target RGB images, leaving a gap for RAW images. To address this issue, we propose the first deep learning-based RAWImage Watermarking (RAWIW) framework for copyright protection. Unlike RGB image watermarking, our method achieves cross-domain copyright protection. We directly embed copyright information into RAW images, which can be later extracted from the corresponding RGB images generated by different post-processing methods. To achieve end-to-end training of the framework, we integrate a neural network that simulates the Image Signal Processing (ISP) pipeline to handle the RAW-to-RGB conversion process. To further validate the generalization of our framework to traditional ISP pipelines and its robustness to transmission distortion, we adopt a distortion network. This network simulates noises introduced during the traditional ISP pipeline and transmission. Our extensive experiments demonstrate that RAWIW successfully achieves cross-domain copyright protection for RAW images while maintaining their visual quality and robustness to ISP pipeline distortions.

Commutative encryption and watermarking algorithm based on compound chaotic systems and zero-watermarking for vector map

Commutative encryption and watermarking (CEW) is an efficient and safe protection technology combining cryptography and digital watermarking. It features the dual functions of secure transmission and copyright safeguarding. In traditional CEW algorithms for vector map, the watermark is embedded by modifying the host data, which cannot meet the needs of high precision vector map, especially for vector maps applied in military and navigation positioning fields. To fill this gap, a CEW algorithm for vector map based on zero-watermarking and permutation encryption is proposed. Firstly, compound chaotic systems are applied to permutation encryption to encrypt the storage order and position of the vertex coordinates respectively. The key is generated by the SHA-256 hash method, which has higher security. Secondly, the number of vertex features is regarded as the feature invariants to establish the watermark information and watermark index. After that, the zero-watermarking image is constructed through the XOR operation of the feature matrix and the copyright information matrix. Experiments show that the combination of permutation encryption and zero watermark can better meet the needs of secure transmission and copyright protection of high-precision vector graphics. The security, robustness and watermark capacity of the algorithm have been improved, making it more practical.

Research on the Construction of Vocal Music Teaching Resources Integration and Sharing Platform in Colleges and Universities under the Background of Modern Science and Technology

Abstract The construction of the teaching resources sharing platform provides high-quality teaching resource retrieval and browsing channels for teachers and students in schools and the majority of off-campus users. This paper combines the functional objectives of the sharing platform design, analyzes the functional requirements of the sharing platform, and designs the overall functional framework of the platform with the advantage of cloud platform technology architecture. Gagne's classification theory of learning results is used to categorize vocal music teaching resources. Blockchain copyright protection technology has been selected to manage the digital resources of vocal music and optimize the privacy protection of users and resource copyright information. Design a user-friendly interface, update the learning interest model using the user interest model, form a personalized recommendation model based on students’ learning interests, and design a sharing scheme for vocal music teaching resources. Test the response performance of blockchain copyright protection and analyze the service impact of a college vocal music teaching resource-sharing platform from the perspective of learning support service. Most of the students have learning communities in the learning process, and the learning communities give the learners a “common identity” and emotional support services so as to achieve the purpose of the construction of a college vocal music teaching resources sharing platform.

A hybrid domain-based watermarking for vector maps utilizing a complementary advantage of discrete fourier transform and singular value decomposition

Digital watermarking plays a crucial role in the copyright protecting of vector maps. Due to its solid theoretical foundation, Discrete Fourier transform (DFT) is frequently used in the construction of watermarking scheme for diverse electronic data. However, when applied to vector maps, DFT is particularly vulnerable to local changes in coordinate points, posing challenges in surviving coordinate point attacks and limiting its practicality. Furthermore, its resistance against geometric attacks is quite weak. To address these issues, we propose an algorithmic complementary strategy that enhances the performance of DFT-based vector map watermarking scheme. The proposed scheme is founded upon a hybrid transform domain consisting of DFT and singular value decomposition (SVD). The first step is to extract feature points using the Douglas Peucker algorithm, with the distance threshold set to a relative value to ensure synchronization of feature points throughout scaling. The feature points are then subjected to DFT to obtain magnitude coefficients, which are then further transformed using SVD. By combining the geometric invariance of magnitude coefficients with the singular vectors of SVD, an invariant with rotation, scaling, and translation invariance can be generated. For watermark embedding, the invariant serves as an embedding domain and are multiplied to minimize the impact on the host vector maps. The experimental results reveal that the proposed vector map watermarking has very low disturbance, substantially lower than the comparing algorithms, deriving in excellent invisibility. The scheme can extract watermark images with a normalized correlation value of 1 under a variety of attacks, including rotation, scaling, translation, vertex addition, simplification, and map cropping, and it can even extract identifiable copyright information under a 10% vertex deletion, demonstrating very comprehensive robustness.

Visible watermarking for 3D models based on 3D Boolean operation

With the continuous expansion of the application field of 3D models and the convenience of model storage, transmission, and replication, the problems of illegal reproduction, illegal modification, and embezzlement faced by 3D models are gradually becoming severe, which has brought substantial economic losses to the producers of 3D models. Therefore, this study aims to develop a visible watermarking method for 3D model using 3D Boolean operation. First, based on TrueType font library, the 3D model of the watermark is generated through the extraction, positioning and combination of character outline. Next, the spatial registration between the watermark model and the 3D model was carried out according to the embedding position of the watermark. Finally, based on multiple 3D Boolean operations, the copyright information of the 3D watermark model is embedded into the 3D model in various modes, such as embossing, debossing, and surface mesh embedding. The experiments demonstrate that the method of this paper supports embedding watermark information in B-rep, CSG, and voxel models. This method can readily support business applications for visible watermarking of 3D models and has laid a good foundation for researching multipurpose 3D model watermarking algorithms supporting copyright notification and protection.