Recent groundbreaking developments on 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 of the generative model. We address these shortcomings by proposing 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, outperforming the existing methods.

翻译：近期生成模型领域的突破性进展引发了人们对实用型单模型归因的兴趣。这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盗窃。然而，现有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。我们通过提出FLIPAD方法解决了这些缺陷，这是一种基于最终层反演和异常检测的开放世界单模型归因新方法。研究表明，所采用的最终层反演可简化为凸套索优化问题，使得该方法在理论上严谨且计算高效。理论发现辅以实验研究，证明了该方法优于现有方法的有效性。