A Dual Model Watermarking Framework for Copyright Protection in Image Processing Networks

Image processing networks have gained great success in many fields,and thus the issue of copyright protection for image processing networks hasbecome a focus of attention. Model watermarking techniques are widely usedin model copyright protection, but there are two challenges: (1) designinguniversal trigger sample watermarking for different network models is stilla challenge;(2) existing methods of copyright protection based on trigger swatermarking are difficult to resist forgery attacks. In this work, we propose adual model watermarking framework for copyright protection in image processingnetworks. The trigger sample watermark is embedded in the trainingprocess of the model, which can effectively verify the model copyright. And wedesign a common method for generating trigger sample watermarks based ongenerative adversarial networks, adaptively generating trigger sample watermarksaccording to different models. The spatial watermark is embedded intothe model output. When an attacker steals model copyright using a forgedtrigger sample watermark, which can be correctly extracted to distinguishbetween the piratical and the protected model. The experiments show that theproposed framework has good performance in different image segmentationnetworks of UNET, UNET++, and FCN (fully convolutional network), andeffectively resists forgery attacks.

Novel copyright protection scheme for digital content

A novel copyright protection scheme for digital content is presented, which is a private watermarking scheme based on the watermark embedding in the DCT domain and watermark extraction Using independent component analysis （ICA）. The system includes the key for watermark extraction and the host image. The algorithm splits the original image into blocks and classifies these blocks based on visual masking, that is, noise visibility function （NVF）. Watermark components with different strength are inserted into chosen direct current components of DCT coefficients according to the classifier. The watermark extraction is based on the characteristic of the statistic independence of the host image, watermark and key. Principle component analysis （PCA） whitening process and FastICA techniques are introduced to ensure a blind watermark extraction without requiring the original image. Experirnental results show the proposed technique is robust under attacks such as image filtering and adding noise, cropping and resizing. In addition, the proposed private watermarking system can be improved to the application of the DTV content protection system.

NEW APPROACH TO COPYRIGHT PROTECTION OF IMAGE AGAINST GEOMETRICAL ATTACKS

To protect the copyright of the image as well as the image quality, a kind of image zero-watermark method based on the Krawtchouk moment invariants and timestamp is proposed. A method is used to protect the image, in which features are drawn out from the image as the watermarking. The main steps of the method are presented. Firstly, some low-order moment invariants of the image are extracted. Secondly, the moment invariants and the key are registered to a fair third party to gain the timestamp. Finally, the timestamp can be used to prove who the real owner is. The processing method is simple, only with a few low-order moment invariants to be computed. Experimental results are obtained and compared with those of the method based on geometric moment invariants. Results show that the scheme can well withstand such geometrical attacks as rotating, scaling, cropping, combined attack, translating, removing lines, filtering, and JPEG lossy compression.

Transparent System for Digital Media Copyright Protection in Mobile Business

At present, in mobile business, the secure environment in the terminal of users has not been embedded, many cryptology-based methods can not be directly used to protect the copyright of digital media. Under this situation, a transparent system based on watermark for digital right management and digital copyright protection is proposed in this paper. The transparent system is called WDRM （watermark-based digital rights management） and transparent to users. Its core is WDRM Agent. In this paper, system inner model and procession are designed in detail at first, including the registration, the downloading, the super distribution, and the cooperation. Because the watermark embedding and the extraction algorithms are the kernel in this architecture, we also discuss how to choose and design the algorithms. Then we establish a package for all algorithms in WDRM Agent.

Research on Copyright Protection Method of Material Genome Engineering Data Based on Zero-Watermarking

In order to effectively solve the problem of copyright protection of materials genome engineering data,this paper proposes a method for copyright protection of materials genome engineering data based on zero-watermarking technology.First,the important attribute values are selected from the materials genome engineering database;then,use the method of remainder to group the selected attribute values and extract eigenvalues;then,the eigenvalues sequence is obtained by the majority election method;finally,XOR the sequence with the actual copyright information to obtain the watermarking information and store it in the third-party authentication center.When a copyright dispute requires copyright authentication for the database to be detected.First,the zero-watermarking construction algorithm is used to obtain an eigenvalues sequence;then,this sequence is XORed with the watermarking information stored in the third-party authentication center to obtain copyright information to-be-detected.Finally,the ownership is determined by calculating the similarity between copyright information to-be-detected and copyright information that has practical significance.The experimental result shows that the zero-watermarking method proposed in this paper can effectively resist various common attacks,and can well achieve the copyright protection of material genome engineering database.

A Image Copyright Protection Method Using Zero-Watermark by Blockchain and IPFS

Behind the popularity of multimedia technology,the dispute over image copyright is getting worse.In the digital watermark prevention technology for copyright infringement,watermark technology is considered to be an important technology to overcome data protection problems and verify the relationship between data ownership.Among the many digital watermarking technologies,zero watermarking technology has been favored in recent years.However,the existing zero watermark technology in the implementation process often needs a trusted third party to store watermarks,which may make the data too central,data storage security is low and copyright registration costs are too high,which creates a rare problem.The decentivization and information cannot be tampered of blockchain technology’s nature find new methods for image copyright protection.This paper studies the role of zero watermark algorithm in the image copyright and its complete storage and certification scheme,proposes a zero watermark image protection framework based on blockchain,and builds a system according to the framework.Combined with blockchain and zero watermarking technology,the framework uses inter IPFS(Inter Planetary File System)to solve the problem of blockchain efficient storage and sharing of large files.In addition,the application of user copyright information,image image query and image trading in the system are realized based on smart contracts,which solves the problem of lack of trusted third parties.Experiments show that the scheme is feasible and robust to various attacks.

Semi-Fragile Watermarking for Copyright Protection and Image Authentication

In this paper, we propose a semi-fragile wattr-marking technology forcopyright protection and image authentication We transform the image into wavelet domain and groupthe four adjacent wavelet coefficients Utilizing the characteristics of the humanvisual system, weembed a digital signal into the average of the four adjacent wavelet coefficients since the mean hasbetter stability than single wavelet coefficient. This method neednt original image when extractsthe watermark. Experimental results show the effectiveness of this method which is robust to commonimage process and fragile to malicious attack.

A TTP-Independent Watermarking Protocol Based on Commutative Cryptosystem for Copyright Protection in E-Commerce

For the purpose of deterring unauthorized duplication and distribution of multimedia contents in e-commerce, some Buyer-seller watermarking protocols which combine of traditional watermarking and fingerprinting techniques have been proposed, However, previous protocols have potential risk from trust third party （TTP） because all entities （including multi-buyers and multi-sellers） mentioned in protocol construct a star tupology like connection, in which the watermark certification authority （WCA） serves as the central point in the interaction and therefore its availability probably turns out to be the vital bottleneck of reliability and efficiency to the whole system. Secondly, WCA holds entire information about watermark used hy Buyers so that an innocent Buyer can he found as guilty if WCA collude with the Seller. In this paper, we propose a watermarking protocol to address the problems using cryptographic technologies in phase of watermark generation. The resuh is a TTP-independent and collusion-secure buyer-seller watermarking protocol.

Research on Software Design of Image Copyright Protection in the Field of Education

Nowadays, image copyright protection is one of the key points of information security in the field of education. Based on the transient property of human vision, the anti-theft and copyright protection strategies are proposed based on the idea of animation synthesis. In this paper, experiments are designed and compared from multiple perspectives. The results show that the strategy based on animation synthesis can not only ensure the browsing effect of images, but also effectively achieve the purpose of preventing interception via screenshot and protecting the legitimate rights of the original images.

A Novel Digital Image Watermarking Scheme on Copyright Protection Based on Network Manufacture Environment

In this paper,we propose a novel wavelet-domain digital image watermarking scheme on copyright protection based on network manufacture environment.It codes the watermarking with error correcting coding and encrypts the watermarking with chaotic encryption.It embeds the watermarking into the coefficients which have large absolute values in the middle-frequency parts got by Discrete Wavelet Transform (DWT) repeatedly.The extraction doesn’t need the original image.Experiment results show that the proposed scheme is easy to implement,and has good robustness to some attacks,such as JPEG compression,average filtering,median filtering,wiener filtering,pepper (?) salt noise,especially to cropping and scaling.In order to solve the prob- lem of the copyright protection of the network manufacture production,the problems of digital image production such as tamper preventing and watermarking attacks preventing and so on are discussed.It solves the problems of manufacture information such as secure exchange and transmissions and production copyright protection and so on.

Lightweight Super-Resolution Model for Complete Model Copyright Protection

Deep learning based techniques are broadly used in various applications, which exhibit superior performance compared to traditional methods. One of the mainstream topics in computer vision is the image super-resolution task. In recent deep learning neural networks, the number of parameters in each convolution layer has increased along with more layers and feature maps, resulting in better image super-resolution performance. In today’s era, numerous service providers offer super-resolution services to users, providing them with remarkable convenience. However, the availability of open-source super-resolution services exposes service providers to the risk of copyright infringement, as the complete model could be vulnerable to leakage. Therefore, safeguarding the copyright of the complete model is a non-trivial concern. To tackle this issue, this paper presents a lightweight model as a substitute for the original complete model in image super-resolution. This research has identified smaller networks that can deliver impressive performance, while protecting the original model’s copyright. Finally, comprehensive experiments are conducted on multiple datasets to demonstrate the superiority of the proposed approach in generating super-resolution images even using lightweight neural networks.

Weakly Supervised Object Localization with Background Suppression Erasing for Art Authentication and Copyright Protection

The problem of art forgery and infringement is becoming increasingly prominent,since diverse self-media contents with all kinds of art pieces are released on the Internet every day.For art paintings,object detection and localization provide an efficient and ef-fective means of art authentication and copyright protection.However,the acquisition of a precise detector requires large amounts of ex-pensive pixel-level annotations.To alleviate this,we propose a novel weakly supervised object localization(WSOL)with background su-perposition erasing(BSE),which recognizes objects with inexpensive image-level labels.First,integrated adversarial erasing(IAE)for vanilla convolutional neural network(CNN)dropouts the most discriminative region by leveraging high-level semantic information.Second,a background suppression module(BSM)limits the activation area of the IAE to the object region through a self-guidance mechanism.Finally,in the inference phase,we utilize the refined importance map(RIM)of middle features to obtain class-agnostic loc-alization results.Extensive experiments are conducted on paintings,CUB-200-2011 and ILSVRC to validate the effectiveness of our BSE.

Few-Shot Graph Classification with Structural-Enhanced Contrastive Learning for Graph Data Copyright Protection

Open-source licenses can promote the development of machine learning by allowing others to access,modify,and redistribute the training dataset.However,not all open-source licenses may be appropriate for data sharing,as some may not provide adequate protections for sensitive or personal information such as social network data.Additionally,some data may be subject to legal or regulatory restrictions that limit its sharing,regardless of the licensing model used.Hence,obtaining large amounts of labeled data can be difficult,time-consuming,or expensive in many real-world scenarios.Few-shot graph classification,as one application of meta-learning in supervised graph learning,aims to classify unseen graph types by only using a small amount of labeled data.However,the current graph neural network methods lack full usage of graph structures on molecular graphs and social network datasets.Since structural features are known to correlate with molecular properties in chemistry,structure information tends to be ignored with sufficient property information provided.Nevertheless,the common binary classification task of chemical compounds is unsuitable in the few-shot setting requiring novel labels.Hence,this paper focuses on the graph classification tasks of a social network,whose complex topology has an uncertain relationship with its nodes'attributes.With two multi-class graph datasets with large node-attribute dimensions constructed to facilitate the research,we propose a novel learning framework that integrates both meta-learning and contrastive learning to enhance the utilization of graph topological information.Extensive experiments demonstrate the competitive performance of our framework respective to other state-of-the-art methods.

Research on IPFS Image Copyright Protection Method Based on Blockchain

In the digital information age,distributed file storage technologies like the InterPlanetary File System(IPFS)have gained considerable traction as a means of storing and disseminating media content.Despite the advantages of decentralized storage,the proliferation of decentralized technologies has highlighted the need to address the issue of file ownership.The aim of this paper is to address the critical issues of source verification and digital copyright protection for IPFS image files.To this end,an innovative approach is proposed that integrates blockchain,digital signature,and blind watermarking.Blockchain technology functions as a decentralized and tamper-resistant ledger,recording and verifying the source information of files,thereby establishing credible evidence of file origin.A digital signature serves to authenticate the identity and integrity of the individual responsible for uploading the file,ensuring data security.Furthermore,blind watermarking is employed to embed invisible information within images,thereby safeguarding digital copyrights and enabling file traceability.To further optimize the efficiency of file retrieval within IPFS,a dual-layer Distributed Hash Table(DHT)indexing structure is proposed.This structure divides file index information into a global index layer and a local index layer,significantly reducing retrieval time and network overhead.The feasibility of the proposed approach is demonstrated through practical examples,providing an effective solution to the copyright protection issues associated with IPFS image files.

MBE:A Music Copyright Depository Framework Incorporating Blockchain and Edge Computing

Audio copyright is a crucial issue in the music industry,as it protects the rights and interests of creators and distributors.This paper studies the current situation of digital music copyright certification and proposes a music copyright certification framework based on“blockchain+edge computing mode,”abbreviated as MBE,by integrating edge computing into the Hyperledger Fabric system.MBE framework compresses and splits the audio into small chunks,performs Fast Fourier Transform(FFT)to extract the peak points of each frequency and combines them to obtain unique audio fingerprint information.After being confirmed by various nodes on the Fabric alliance chain,audio fingerprint information and copyright owner information are recorded on the chain and broadcast to all participants.Blockchain technology’s characteristics of being tamper-proof and traceable not only reform the trust mechanism of copyright protection but also endow edge computing with the ability to resist tampering and single-point attack,greatly enhancing the robustness of the music copyright certification system.Meanwhile,edge computing mode improves Fabric blockchain’s processing speed and transaction throughput.Experimental results show that MBE’s performance is better than traditional systems regarding efficiency,storage demand and security.Compared to the traditional Fabric system without edge computing mode,MBE exhibits a 53%higher deposition efficiency and a 48%lower storage space requirement.

数字图像多功能水印综述

数字图像多功能水印技术通过将多种类型的水印嵌入到同一图像,以实现2种及以上的图像保护功能(版权保护、内容认证、篡改恢复等).当前,数字图像多功能水印算法仍面临着性能和应用场景等方面的挑战.为促进图像多功能水印的进一步发展,首先系统地梳理了数字图像水印技术,包括其系统特性、常见分类、关键技术与成果,以及其可实现的功能与应用场景;在此基础上,对近10年来不同类别的多功能水印嵌入模型(融合水印嵌入、零水印嵌入、水印串行嵌入、水印并行嵌入)的相关工作进行归纳与分析;最后,总结了当前数字图像多功能水印技术的不足,展望了后续研究与发展趋势.综合分析发现,多水印的嵌入难免对水印综合性能造成损失,且多功能水印在真实应用场景中的效果仍待改善.人工智能等新技术在数字图像多功能水印中的应用有望提升算法性能.

数字出版供应链下区块链投资与版权保护策略

区块链技术的可追溯性、安全性以及不可篡改性可以解决数字内容产品的盗版问题。以此为背景,构建由出版商、销售平台以及消费者构成的数字出版供应链,探讨在传统版权保护和区块链下的版权保护两种版权保护模式中的出版商最优定价和平台最优版权保护强度的决策问题,并比较了供应链绩效。研究发现,区块链下的版权保护模式中平台实施严格的版权保护强度,进而提高正版产品的吸引力,需求上升弥补了传统版权保护下出版商通过降价提高需求造成的利润损失。但是,平台只有在区块链投资成本较低时才愿意投资区块链,无法实现供应链的收益最大化。针对这一问题,通过设计契约提高了平台投资区块链的动机。最后,研究了平台投资区块链对购买正版产品消费者的消费者剩余以及社会福利,发现投资区块链可以提高社会福利,但在一定程度上会损害正版产品的消费者剩余。

数字出版平台的版权困境及纾解路径研究

我国正处在推动出版行业转型升级、加快数字中国建设的关键时期。侵权盗版行为与平台版权垄断构成了阻碍数字出版行业可持续发展的痼疾。数字出版领域版权保护困境的深层原因包括法律保护手段滞后于数字出版业发展中的新问题、流量机制利导下默许盗版传播以扩大影响力、版权垄断下平台与创作者间存在不对等关系等。本文在此基础上提出引入版权信用机制协同治理侵权困境、完善创作者按要素参与分配的利益机制、推动新时代数字出版领域版权法治建设、共赢生态位下实现数字出版物的长远发展等建议,以期推动构建“共建、共治、共享”的数字出版格局,促进数字出版行业的健康发展。