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对实用型单模型归属判定技术的关注。此类方法用于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，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非理想化的修改。为克服这些缺陷，我们首先从异常检测视角审视单模型归属判定问题。基于这一视角转变，我们提出FLIPAD方法——一种基于最终层反演与异常检测的开放世界单模型归属判定新方案。我们证明所采用的最终层反演可转化为凸套索优化问题，使该方法兼具理论严谨性与计算高效性。理论发现辅以实验研究，验证了该方法在多个领域的有效性及其跨领域适应性。