  Digital images are ubiquitous in our modern lives, with uses ranging from
social media to news, and even scientific papers. For this reason, it is
crucial evaluate how accurate people are when performing the task of identify
doctored images. In this paper, we performed an extensive user study evaluating
subjects capacity to detect fake images. After observing an image, users have
been asked if it had been altered or not. If the user answered the image has
been altered, he had to provide evidence in the form of a click on the image.
We collected 17,208 individual answers from 383 users, using 177 images
selected from public forensic databases. Different from other previously
studies, our method propose different ways to avoid lucky guess when evaluating
users answers. Our results indicate that people show inaccurate skills at
differentiating between altered and non-altered images, with an accuracy of
58%, and only identifying the modified images 46.5% of the time. We also track
user features such as age, answering time, confidence, providing deep analysis
of how such variables influence on the users' performance.
