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属溯源方法的兴趣。此类方法旨在判断样本是否由特定生成器所产生，例如用于证明知识产权侵权。然而，先前研究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。我们首先通过异常检测的视角重新审视单模型归属溯源问题，从而解决了这些缺陷。基于这一视角转变，我们提出了FLIPAD——一种在开放世界设定下基于最终层逆映射与异常检测的单模型归属溯源新方法。我们证明了所采用的最终层逆映射可简化为凸LASSO优化问题，这使得我们的方法在理论上严谨且计算高效。理论发现得到了实验研究的佐证，该研究证明了我们方法的有效性及其跨领域的适应能力。