LUCID Rights: Leveraging Uncertainty in Content & Metadata to Enable Indelible Digital Rights

创意内容是当今大多数在线消费者活动无可争议的驱动力和焦点。然而，由于不允许图像和视频内容与版权和所有权细节进行独特而有力的联系，许多当前和过去的商业价值都将丧失。此外，在专业媒体行业，供应链遇到了复杂的版权问题，导致80%的内容没有“版权准备好”，或者在商业上不可行。虽然乍一看，这个问题似乎可以用现成的组件、故意混淆或内容和权限的意外变化（统称为“不确定性”）来解决，但传统的在线搜索工具不能很好地解决这个问题。这导致了目前的情况，即在线内容的权利发现是一个人工的、容易出错的、繁琐的过程，需要付出大量的努力才能保持在法律范围内，而对于那些希望违反版权法的人来说，几乎不需要付出任何努力（风险最小）。LUCID Rights项目汇集了一个由机器学习、知识发现、高性能图像和视频工程、版权法以及商业和内容许可模式等领域的国际领先专家组成的跨学科团队，以应对这一重要挑战。关键目标是为内容属性和版权信息的权利发现和不可磨灭签名创建创建一个独特的解决方案，该解决方案将对权利描述中的噪声或不确定性具有鲁棒性。机器可读合同和紧凑签名生成标准的融合允许在符合标准的机制中进行试验，从而在短时间内实现开放性和商业吸引力。这将首次允许应用先进的机器学习来破坏数字内容版权领域，从而释放英国国内外新市场和服务的潜力。

Creative content is the undisputed driving force and focal point of the majority of online consumer activity today. However, a lot of the current and past commercial value is lost by not allowing for image and video content to be uniquely and robustly linked to copyright and ownership details. Further, in the professional media industry, supply chains encounter complex rights issues which cause 80% of all content not to be 'rights ready', or commercially viable. While at a first glance this seems like a problem that can be addressed with off-the-shelf components, deliberate obfuscations or accidental variations in content and rights (collectively called "uncertainty"), do not allow for conventional online search tools to work well for this problem. This leads to the current situation where rights discovery for online content is a manual, error prone and cumbersome process, with substantial effort required to remain within the law, and virtually no effort (and minimum risk) for those that wish to violate copyright law. The LUCID Rights project brings together an interdisciplinary team of internationally-leading experts in machine learning, knowledge discovery, high-performance image & video engineering, copyright law, and business & content licensing models in order to address this important challenge. The key objective is to create a unique solution for rights discovery and indelible signature creation for the content properties and copyright information that will be robust to noise or uncertainty in the rights description. The confluence of standards for machine-readable contracts and compact signature generation allows for this to also be trialed within standard-compliant mechanisms, thereby enabling openness, and commercial traction within short timeframes. This will allow for the first time to apply advanced machine learning to disrupt the domain of digital content rights, thereby unlocking the potential for new markets and services within the UK and internationally.