  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.
