  Creating high-quality and realistic images is now possible thanks to the
impressive advancements in image generation. A description in natural language
of your desired output is all you need to obtain breathtaking results. However,
as the use of generative models grows, so do concerns about the propagation of
malicious content and misinformation. Consequently, the research community is
actively working on the development of novel fake detection techniques,
primarily focusing on low-level features and possible fingerprints left by
generative models during the image generation process. In a different vein, in
our work, we leverage human semantic knowledge to investigate the possibility
of being included in frameworks of fake image detection. To achieve this, we
collect a novel dataset of partially manipulated images using diffusion models
and conduct an eye-tracking experiment to record the eye movements of different
observers while viewing real and fake stimuli. A preliminary statistical
analysis is conducted to explore the distinctive patterns in how humans
perceive genuine and altered images. Statistical findings reveal that, when
perceiving counterfeit samples, humans tend to focus on more confined regions
of the image, in contrast to the more dispersed observational pattern observed
when viewing genuine images. Our dataset is publicly available at:
https://github.com/aimagelab/unveiling-the-truth.
