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Humans are easily fooled by digital images
- Source :
- Computers & Graphics. 68:142-151
- Publication Year :
- 2017
- Publisher :
- Elsevier BV, 2017.
-
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.
- Subjects :
- FOS: Computer and information sciences
Information retrieval
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
Computer Science - Human-Computer Interaction
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
General Engineering
020207 software engineering
Subject (documents)
02 engineering and technology
Computer Graphics and Computer-Aided Design
Graphics (cs.GR)
Human-Computer Interaction (cs.HC)
Image (mathematics)
Human-Computer Interaction
Digital image
Computer Science - Graphics
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Social media
Digital image forensics
Subjects
Details
- ISSN :
- 00978493
- Volume :
- 68
- Database :
- OpenAIRE
- Journal :
- Computers & Graphics
- Accession number :
- edsair.doi.dedup.....21951274bc5a977b41e65fa2ab2ca491