As generative models continue to evolve, detecting AI-generated images remains a critical challenge. While effective detection methods exist, they often lack formal interpretability and may rely on implicit assumptions about fake content, potentially limiting robustness to distributional shifts. In this work, we introduce a rigorous, statistically grounded framework for fake image detection that focuses on producing a probability score interpretable with respect to the real-image population. Our method leverages the strengths of multiple existing detectors by combining training-free statistics. We compute p-values over a range of test statistics and aggregate them using classical statistical ensembling to assess alignment with the unified real-image distribution. This framework is generic, flexible, and training-free, making it well-suited for robust fake image detection across diverse and evolving settings.

翻译：随着生成模型的不断发展，检测人工智能生成的图像仍然是一个关键挑战。虽然存在有效的检测方法，但它们往往缺乏形式化的可解释性，并且可能依赖于对虚假内容的隐含假设，这可能会限制其对分布变化的鲁棒性。在本工作中，我们引入了一个严谨的、基于统计学的虚假图像检测框架，其核心在于生成一个可相对于真实图像总体进行解释的概率分数。我们的方法通过结合无需训练的统计量，充分利用了多种现有检测器的优势。我们计算一系列检验统计量的p值，并使用经典的统计集成方法对其进行聚合，以评估其与统一的真实图像分布的一致性。该框架具有通用性、灵活性且无需训练，非常适合在多样且不断变化的环境中实现鲁棒的虚假图像检测。