Humans Are Easily Fooled by Digital Images
This addresses the critical issue of image authenticity for general users in contexts like social media and news, but it is incremental as it builds on prior forensic studies with improved evaluation methods.
The study tackled the problem of evaluating human ability to detect doctored digital images, finding that people are inaccurate with an overall accuracy of 58% and only identify modified images 46.5% of the time.
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.