A generic passive image forgery detection scheme using local binary pattern with rich models. (August 2017)

• Record Type: Journal Article
• Authors: Farooq, Sundus; Yousaf, Muhammad Haroon; Hussain, Fawad
• Is Part Of: Computers & electrical engineering. Volume 62(2017)
• Page Start: 459
• Page End: 472
• Publication Date: 2017-08
• Subjects: Forgery -- Universal forensics -- Tampering detection -- Steganalysis -- Rich models; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.05.008
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A generic passive image forgery scheme is proposed using spatial rich model (SRM). Local binary patterns are employed in rich models for intense textural statistics. Performance of proposed models is observed using 5 sub-model selection strategies. Using these models as features, 11 forgeries are classified with > 90% accuracy. Abstract: Image forgery detection is one of the prominent areas from research and development perspective. This research work aims to propose a scheme for the detection of multiple types of image forgeries. In this paper, a generic passive image forgery scheme is proposed using spatial rich model (SRM) in combination with textural feature i.e. local binary pattern (LBP). Moreover, different sub-model selection strategies are implemented and analyzed to investigate the performance-to-model dimensionality trade-off. Ensemble multi-class classifier is used for classifying the features into different forgery classes. The proposed scheme is evaluated on the dataset generated from IEEE IFS-TC image forensics challenge containing 10 different kinds of forgeries. The results reveal that computing LBP on noise residuals in conjunction with co-occurrence matrices using BEST-q-CLASS feature selection strategy produces a model which performs efficiently for almost any set of modifications with accuracy of 98.4%. Graphical abstract: