Recent breakthroughs in generative modeling have sparked interest in practical single-model attribution. Such methods predict whether a sample was generated by a specific generator or not, for instance, to prove intellectual property theft. However, previous works are either limited to the closed-world setting or require undesirable changes to the generative model. We address these shortcomings by, first, viewing single-model attribution through the lens of anomaly detection. Arising from this change of perspective, we propose FLIPAD, a new approach for single-model attribution in the open-world setting based on final-layer inversion and anomaly detection. We show that the utilized final-layer inversion can be reduced to a convex lasso optimization problem, making our approach theoretically sound and computationally efficient. The theoretical findings are accompanied by an experimental study demonstrating the effectiveness of our approach and its flexibility to various domains.

翻译：生成建模领域的最新突破引发了人们对实用单模型归因的兴趣。此类方法旨在预测样本是否由特定生成器生成，例如用于证明知识产权侵权。然而，先前的工作要么局限于封闭世界设定，要么需要对生成模型进行不理想的修改。我们通过以下方式解决这些缺陷：首先，从异常检测的视角审视单模型归因问题。基于这一视角转变，我们提出FLIPAD——一种基于最后一层反演与异常检测的开放世界设定下单模型归因新方法。我们证明所采用的最后一层反演可归结为凸套索优化问题，这使得我们的方法在理论上可靠且计算高效。理论发现还辅以实验研究，证明了该方法在不同领域中的有效性与灵活性。