Arabian Journal for Science and Engineering

, Volume 43, Issue 2, pp 555–568 | Cite as

Subsampling-Based Blind Image Forgery Detection Using Support Vector Machine and Artificial Neural Network Classifiers

  • Gajanan K. Birajdar
  • Vijay H. Mankar
Research Article - Computer Engineering and Computer Science


In order to create convincing doctored images, forged images are exposed to some linear transformations (like rotation and resizing) which involve a resampling step. In copy–paste image forgery, the pasted portion is rescaled in order to hide traces of malicious tampering. In this paper, an algorithm is proposed to detect the global resizing operation of the doctored image blindly based on features extracted using subsampling. Fisher criterion is employed in order to choose the relevant features and reduce the dimensionality of the statistical features. Support vector machine (SVM) and multi-layer feedforward artificial neural network (ANN) are used for classification. Experimental results using \(C_b\), \(C_r\) and grayscale images demonstrate that the proposed method has good rescaling detection performance even when dealing with distortions like JPEG compression. The results indicate that SVM performs better compared to ANN classifier.


Image forgery detection Image forensic Rescaling detection Artificial neural network classifier Subsampling 


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Copyright information

© King Fahd University of Petroleum & Minerals 2017

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringPriyadarshini Institute of Engineering and TechnologyNagpurIndia
  2. 2.Department of Electronics EngineeringRamrao Adik Institute of TechnologyNerul, Navi MumbaiIndia
  3. 3.Department of Electronics and TelecommunicationGovernment PolytechnicAhmednagarIndia

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