The social media-fuelled explosion of fake news and misinformation supported by tampered images has led to growth in the development of models and datasets for image manipulation detection. However, existing detection methods mostly treat media objects in isolation, without considering the impact of specific manipulations on viewer perception. Forensic datasets are usually analyzed based on the manipulation operations and corresponding pixel-based masks, but not on the semantics of the manipulation, i.e., type of scene, objects, and viewers' attention to scene content. The semantics of the manipulation play an important role in spreading misinformation through manipulated images. In an attempt to encourage further development of semantic-aware forensic approaches to understand visual misinformation, we propose a framework to analyze the trends of visual and semantic saliency in popular image manipulation datasets and their impact on detection.

翻译：社交媒体推动的虚假新闻和错误信息爆炸式增长，往往伴随着篡改图像的支持，这促进了图像篡改检测模型与数据集的快速发展。然而，现有的检测方法大多孤立地处理媒体对象，未考虑特定篡改操作对观看者感知的影响。取证数据集的分析通常基于篡改操作及相应的基于像素的掩码，而非基于篡改的语义——即场景类型、对象以及观看者对场景内容的注意力。篡改的语义在通过篡改图像传播错误信息的过程中起着重要作用。为了鼓励进一步发展语义感知的取证方法以理解视觉错误信息，我们提出了一个框架，用于分析流行图像篡改数据集中视觉与语义显著性的趋势及其对检测的影响。