Text-to-video artificial intelligence(AI)is a new product that has arisen from the continuous development of digital technology over recent years.The emergence of various text-to-video AI models,including Sora,is driv...Text-to-video artificial intelligence(AI)is a new product that has arisen from the continuous development of digital technology over recent years.The emergence of various text-to-video AI models,including Sora,is driving the proliferation of content generated through concrete imagery.However,the content generated by text-to-video AI raises significant issues such as unclear work identification,ambiguous copyright ownership,and widespread copyright infringement.These issues can hinder the development of text-to-video AI in the creative fields and impede the prosperity of China’s social and cultural arts.Therefore,this paper proposes three recommendations within a legal framework:(a)categorizing the content generated by text-to-video AI as audiovisual works;(b)clarifying the copyright ownership model for text-to-video AI works;(c)reasonably delineating the responsibilities of the parties who are involved in the text-to-video AI works.The aim is to mitigate the copyright risks associated with content generated by text-to-video AI and to promote the healthy development of text-to-video AI in the creative fields.展开更多
In November 2020,the third amendment of the Copyright Law of the People’s Republic of China was completed and officially implemented in June 2021,which is undoubtedly of great significance to Chinese citizens with a ...In November 2020,the third amendment of the Copyright Law of the People’s Republic of China was completed and officially implemented in June 2021,which is undoubtedly of great significance to Chinese citizens with a growing awareness of copyright.This has also triggered our thinking about the impact of Copyright Law on digital copyright.Through the analysis of the cases after the amendment of the Copyright Law,we find that to a certain extent,the Copyright Law has played a great role in the protection of digital copyright,but it is still unable to achieve comprehensive protection,and there are still some imperfections.From this point of view,the simple legal protection of digital copyright cannot be taken into account.It still needs to be combined with technical protection means,cooperate with social conditions,and work together to create a harmonious and healthy online publishing environment and promote the protection of digital copyright.展开更多
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 used...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.展开更多
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 propo...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.展开更多
基金This research is supported by“Research on Legal Issues Caused by Sora from the Perspective of Copyright Law”(YK20240094)of the Xihua University Science and Technology Innovation Competition Project for Postgraduate Students(cultivation project).
文摘Text-to-video artificial intelligence(AI)is a new product that has arisen from the continuous development of digital technology over recent years.The emergence of various text-to-video AI models,including Sora,is driving the proliferation of content generated through concrete imagery.However,the content generated by text-to-video AI raises significant issues such as unclear work identification,ambiguous copyright ownership,and widespread copyright infringement.These issues can hinder the development of text-to-video AI in the creative fields and impede the prosperity of China’s social and cultural arts.Therefore,this paper proposes three recommendations within a legal framework:(a)categorizing the content generated by text-to-video AI as audiovisual works;(b)clarifying the copyright ownership model for text-to-video AI works;(c)reasonably delineating the responsibilities of the parties who are involved in the text-to-video AI works.The aim is to mitigate the copyright risks associated with content generated by text-to-video AI and to promote the healthy development of text-to-video AI in the creative fields.
文摘In November 2020,the third amendment of the Copyright Law of the People’s Republic of China was completed and officially implemented in June 2021,which is undoubtedly of great significance to Chinese citizens with a growing awareness of copyright.This has also triggered our thinking about the impact of Copyright Law on digital copyright.Through the analysis of the cases after the amendment of the Copyright Law,we find that to a certain extent,the Copyright Law has played a great role in the protection of digital copyright,but it is still unable to achieve comprehensive protection,and there are still some imperfections.From this point of view,the simple legal protection of digital copyright cannot be taken into account.It still needs to be combined with technical protection means,cooperate with social conditions,and work together to create a harmonious and healthy online publishing environment and promote the protection of digital copyright.
基金supported by the National Natural Science Foundation of China under grants U1836208,by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)fundby the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET)fund,China.
文摘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.
基金supported by Jiangxi Provincial Natural Science Foundation under Grant Nos.20224BAB212015,20224ACB202007Jiangxi Province Science and Technology Project (03 Special 5G Project)under Grant No.20224ABC03A13+1 种基金the Foundation of Jiangxi Educational Committee underGrant No.GJJ210338the National Natural Science Foundation of China (NSFC),under Grant No.61962026.
文摘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.
