In this work, we present a decision fusion strategy for image forensics. We define a framework that exploits information provided by available forensic tools to yield a global judgment about the authenticity of an image. Sources of information are modeled and fused using Dempster–Shafer Theory of Evidence, since this theory allows us to handle uncertain answers from tools and lack of knowledge about prior probabilities better than the classical Bayesian approach. The proposed framework permits us to exploit any available information about tools reliability and about the compatibility between the traces the forensic tools look for. The framework is easily extendable: new tools can be added incrementally with a little effort. Comparison with logical disjunction- and SVM-based fusion approaches shows an improvement in classification accuracy, particularly when strong generalization capabilities are needed.

A Framework for Decision Fusion in Image Forensics Based on Dempster-Shafer Theory of Evidence / Marco Fontani; Tiziano Bianchi; Alessia De Rosa; Alessandro Piva; Mauro Barni. - In: IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY. - ISSN 1556-6013. - ELETTRONICO. - 8:(2013), pp. 593-607. [10.1109/TIFS.2013.2248727]

A Framework for Decision Fusion in Image Forensics Based on Dempster-Shafer Theory of Evidence

FONTANI, MARCO;DE ROSA, ALESSIA;PIVA, ALESSANDRO;
2013

Abstract

In this work, we present a decision fusion strategy for image forensics. We define a framework that exploits information provided by available forensic tools to yield a global judgment about the authenticity of an image. Sources of information are modeled and fused using Dempster–Shafer Theory of Evidence, since this theory allows us to handle uncertain answers from tools and lack of knowledge about prior probabilities better than the classical Bayesian approach. The proposed framework permits us to exploit any available information about tools reliability and about the compatibility between the traces the forensic tools look for. The framework is easily extendable: new tools can be added incrementally with a little effort. Comparison with logical disjunction- and SVM-based fusion approaches shows an improvement in classification accuracy, particularly when strong generalization capabilities are needed.
2013
8
593
607
Marco Fontani; Tiziano Bianchi; Alessia De Rosa; Alessandro Piva; Mauro Barni
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/795876
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