Copyright infringement may occur when a generative model produces samples substantially similar to some copyrighted data that it had access to during the training phase. The notion of access usually refers to including copyrighted samples directly in the training dataset, which one may inspect to identify an infringement. We argue that such visual auditing largely overlooks a concealed copyright infringement, where one constructs a disguise that looks drastically different from the copyrighted sample yet still induces the effect of training Latent Diffusion Models on it. Such disguises only require indirect access to the copyrighted material and cannot be visually distinguished, thus easily circumventing the current auditing tools. In this paper, we provide a better understanding of such disguised copyright infringement by uncovering the disguises generation algorithm, the revelation of the disguises, and importantly, how to detect them to augment the existing toolbox. Additionally, we introduce a broader notion of acknowledgment for comprehending such indirect access. Our code is available at https://github.com/watml/disguised_copyright_infringement.

翻译：当生成模型在训练阶段访问了某些受版权保护的数据，并生成与其高度相似的样本时，可能发生版权侵权。通常，"访问"的概念指的是将受版权保护的样本直接纳入训练数据集中，人们可以通过检查该数据集来识别侵权行为。我们认为，这种视觉审计在很大程度上忽视了一种隐蔽的版权侵权形式，即攻击者构建一个与受版权保护样本看起来截然不同、却仍然能产生在潜在扩散模型上训练该样本效果的伪装样本。此类伪装仅需间接访问受版权保护的材料，且无法通过视觉区分，从而轻易规避了当前的审计工具。本文通过揭示伪装生成算法、伪装样本的暴露机制，以及关键地，如何检测它们以增强现有工具箱，来深化对此类隐蔽版权侵权的理解。此外，我们引入了一个更广泛的"认知"概念，以理解这种间接访问。我们的代码可在 https://github.com/watml/disguised_copyright_infringement 获取。