An Investigation into the Issues Concerning the Copyright of Content Generated by Text-to-Video AI

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.

Where Do You Draw the Line In Copyright Infringement?

Famous Chinese film director Chen Kaige has made a big comeback with the release of his visually breathtaking movie The Promise, a $44 million mythological epic said to be the most expensive film in the history of Chinese cinema. As with every project Chen is involved in, the film has drawn extensive attention before and after its screening. Having been given a Golden Globe nomination for

Machine Learning-Based Advertisement Banner Identification Technique for Effective Piracy Website Detection Process

In the contemporary world, digital content that is subject to copyright is facing significant challenges against the act of copyright infringement.Billions of dollars are lost annually because of this illegal act. The currentmost effective trend to tackle this problem is believed to be blocking thosewebsites, particularly through affiliated government bodies. To do so, aneffective detection mechanism is a necessary first step. Some researchers haveused various approaches to analyze the possible common features of suspectedpiracy websites. For instance, most of these websites serve online advertisement, which is considered as their main source of revenue. In addition, theseadvertisements have some common attributes that make them unique ascompared to advertisements posted on normal or legitimate websites. Theyusually encompass keywords such as click-words (words that redirect to installmalicious software) and frequently used words in illegal gambling, illegal sexual acts, and so on. This makes them ideal to be used as one of the key featuresin the process of successfully detecting websites involved in the act of copyrightinfringement. Research has been conducted to identify advertisements servedon suspected piracy websites. However, these studies use a static approachthat relies mainly on manual scanning for the aforementioned keywords. Thisbrings with it some limitations, particularly in coping with the dynamic andever-changing behavior of advertisements posted on these websites. Therefore,we propose a technique that can continuously fine-tune itself and is intelligentenough to effectively identify advertisement (Ad) banners extracted fromsuspected piracy websites. We have done this by leveraging the power ofmachine learning algorithms, particularly the support vector machine with theword2vec word-embedding model. After applying the proposed technique to1015 Ad banners collected from 98 suspected piracy websites and 90 normal orlegitimate websites, we were able to successfully identify Ad banners extractedfrom suspected piracy websites with an accuracy of 97%. We present thistechnique with the hope that it will be a useful tool for various effective piracywebsite detection approaches. To our knowledge, this is the first approachthat uses machine learning to identify Ad banners served on suspected piracywebsites.

Source Code Comparison of DOS and CP/M

In a previous paper [1], I compared DOS from Microsoft and CP/M from Digital Research Inc. (DRI) to determine whether the original DOS source code had been copied from CP/M source code as had been rumored for many years [2] [3]. At the time, the source code for CP/M was publicly available but the source code for DOS was not. My comparison was limited to the comparison of the DOS 1.11 binary code and the source code for CP/M 2.0 from 1981. Since that time, the Computer History Museum in Mountain View, California received the source code for DOS 2.0 from Microsoft and was given permission to make it public. The museum also received the source code for DOS 1.1 from Tim Paterson, the developer who was originally contracted by Microsoft to write DOS. In this paper, I perform a further analysis using the newly accessible source code and determine that no code was copied. I further conclude that the commands were not copied but that a substantial number of the system calls were copied.