DocumentCode :
1848648
Title :
Discrimination between computer generated and natural human faces based on asymmetry information
Author :
Dang-Nguyen, Duc-Tien ; Boato, Giulia ; De Natale, Francesco G B
Author_Institution :
Dept. of Inf. & Comput. Sci., Univ. of Trento, Trento, Italy
fYear :
2012
fDate :
27-31 Aug. 2012
Firstpage :
1234
Lastpage :
1238
Abstract :
The recent development of information and communication technology has made computer software able to create highly realistic multimedia contents that can be, for human, impossible to distinguish from the natural ones. This fact leads to the need for tools and techniques that can reliably discriminate between natural and computer generated multimedia data in forensics applications. In this paper, we focus on the specific class of images containing faces, since we consider critical to be able to discriminate between photographic faces and the photorealistic ones. To this aim, we present a new geometric-based approach relying on face asymmetry information. Experimental results show that asymmetry information could be used as a hint to tackle this problem without requiring classification tools and training or combined with state-of-the-art approaches to improve their performances.
Keywords :
computer forensics; face recognition; image classification; multimedia computing; asymmetry information; computer generated human faces; computer generated multimedia data; computer software; face containing image classification tools; forensics applications; geometric-based approach; information and communication technology; natural data; natural human faces; photographic faces; realistic multimedia contents; state-of-the-art approach; Computers; Estimation; Humans; Lighting; Multimedia communication; Noise; Shape; Computer Generated Multimedia Content; Digital Image Forensics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European
Conference_Location :
Bucharest
ISSN :
2219-5491
Print_ISBN :
978-1-4673-1068-0
Type :
conf
Filename :
6333919
Link To Document :
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