The remarkable generative capabilities of denoising diffusion models have raised new concerns regarding the authenticity of the images we see every day on the Internet. However, the vast majority of existing deepfake detection models are tested against previous generative approaches (e.g. GAN) and usually provide only a "fake" or "real" label per image. We believe a more informative output would be to augment the per-image label with a localization map indicating which regions of the input have been manipulated. To this end, we frame this task as a weakly-supervised localization problem and identify three main categories of methods (based on either explanations, local scores or attention), which we compare on an equal footing by using the Xception network as the common backbone architecture. We provide a careful analysis of all the main factors that parameterize the design space: choice of method, type of supervision, dataset and generator used in the creation of manipulated images; our study is enabled by constructing datasets in which only one of the components is varied. Our results show that weakly-supervised localization is attainable, with the best performing detection method (based on local scores) being less sensitive to the looser supervision than to the mismatch in terms of dataset or generator.

翻译：去噪扩散模型卓越的生成能力引发了人们对互联网上日常所见图像真实性的新担忧。然而，现有绝大多数深度伪造检测模型针对以往的生成方法（如GAN）进行测试，且通常仅提供每张图像的"伪造"或"真实"标签。我们认为更具信息量的输出应是在图像级标签基础上附加定位图，以指示输入的哪些区域被篡改。为此，我们将该任务定义为弱监督定位问题，并识别出三种主要方法类别（基于解释、局部得分或注意力机制），通过采用Xception网络作为通用骨干架构进行公平比较。我们系统分析了参数化设计空间的所有关键因素：方法选择、监督类型、数据集及用于生成篡改图像的生成器；该研究通过构建仅改变单一组件的对比数据集得以实现。结果表明：弱监督定位具有可行性，其中基于局部得分的最佳检测方法对宽松监督的敏感度低于对数据集或生成器不一致的敏感度。