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已引发人们对互联网上日常所见图像真实性的新担忧。然而，现有深度伪造检测模型绝大多数针对早期生成方法（如GAN）进行测试，且通常仅输出每张图像的"伪造"或"真实"标签。我们认为更具信息量的输出方式应为在每张图像标签基础上，补充指示输入图像中被篡改区域的定位图。为此，我们将该任务框架化为弱监督定位问题，确定三种主要方法类别（基于解释、基于局部评分或基于注意力机制），并通过统一使用Xception网络作为公共骨干架构进行公平比较。我们审慎分析了设计参数空间的所有关键因素：方法选择、监督类型、数据集及伪造图像生成所用生成器；通过构建仅变动单一组件的实验数据集展开研究。结果表明，弱监督定位具有可行性，其中基于局部评分的最优检测方法对宽松监督的敏感度低于对数据集或生成器差异的敏感度。