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

  • Research Article - Computer Engineering and Computer Science
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Abstract

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.

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Author notes

  1. Gajanan K. Birajdar

    Present address: Department of Electronics Engineering, Ramrao Adik Institute of Technology, Nerul, Navi Mumbai, Maharashtra, 400706, India

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Priyadarshini Institute of Engineering and Technology, Nagpur, Maharashtra, 440019, India

    Gajanan K. Birajdar

  2. Department of Electronics and Telecommunication, Government Polytechnic, Ahmednagar, Maharashtra, 414001, India

    Vijay H. Mankar

Authors
  1. Gajanan K. Birajdar
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  2. Vijay H. Mankar
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Correspondence to Gajanan K. Birajdar.

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Birajdar, G.K., Mankar, V.H. Subsampling-Based Blind Image Forgery Detection Using Support Vector Machine and Artificial Neural Network Classifiers. Arab J Sci Eng 43, 555–568 (2018). https://doi.org/10.1007/s13369-017-2671-3

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  • DOI: https://doi.org/10.1007/s13369-017-2671-3

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