Humans Are Easily Fooled by Digital Images

TL;DR

This study reveals that people are generally poor at detecting digitally altered images, with only moderate accuracy, highlighting the need for improved tools and training in digital image verification.

Contribution

The paper presents an extensive user study on image forgery detection, introducing methods to reduce lucky guesses and analyzing how user features affect performance.

Findings

Overall detection accuracy is 58%.

Users correctly identify altered images 46.5% of the time.

User features like age and confidence influence detection performance.

Abstract

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.