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Passive Image Manipulation Detection Using Wavelet Transform and Support Vector Machine Classifier

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Proceedings of International Conference on ICT for Sustainable Development

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 408))

Abstract

In this paper, blind global contrast enhancement detection method is proposed using wavelet transform-based features. Wavelet subband energy and statistical features are computed using multilevel 2D wavelet decomposition. Mutual information-based feature selection measure is employed to select the most relevant features while discarding the redundant features. Experimental results are presented using grayscale and G component image database and SVM classifier. Simulation results prove the effectiveness of the proposed algorithm compared to other existing contrast enhancement detection techniques.

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References

  1. Avcibas, I., Bayram, S., Memon, N., Ramkumar, M., & Sankur, B. (2004). A classifier design for detecting image manipulations. In International Conference on Image Processing (pp. 2645–2648) (October 2004).

    Google Scholar 

  2. Bayram, S., Avcibas, I., Sankur, B., & Memon, N. (2005). Image manipulation detection with binary similarity measures. In European Signal Processing Conference (pp. 1–4).

    Google Scholar 

  3. Bayram, S., Avcibas, I., Sankur, B., & Memon, N. (2006). Image manipulation detection. Journal of Electronic Imaging, 15(4), 1–17.

    Article  Google Scholar 

  4. Birajdar, G. K., & Mankar, V. H. (2013). Digital image forgery detection using passive techniques: A survey. Digital Investigation, 10(3), 226–245.

    Article  Google Scholar 

  5. Boato, G., Natale, F., & Zontone, P. (2010). How digital forensics may help assessing the perceptual impact of image formation and manipulation. In International Work-shop on Video Processing and Quality Metrics for Consumer Electronics (pp. 1–6) (January 2010).

    Google Scholar 

  6. Cao, G., Zhao, Y., Ni, R., & Li, X. (2014). Contrast enhancement-based forensics in digital images. IEEE Transactions on Information Forensics and Security, 9(3), 515–525.

    Article  Google Scholar 

  7. Gul, G., Avcibas, I., & Kurugollu, F. (2010). SVD based image manipulation detection. In IEEE International Conference on Image Processing (pp. 1765–1768) (September 2010).

    Google Scholar 

  8. Lin, X., Li, C. T., & Hu, Y. (2013). Exposing image forgery through the detection of contrast enhancement. In International Conference on Image Processing (pp. 4467–4471) (September 2013).

    Google Scholar 

  9. Lin, X., Wei, X., & Li, C. T. (2014). Two improved forensic methods of detecting contrast enhancement in digital images. In Media Watermarking, Security, and Forensics, Vol. 9028 (February 2014).

    Google Scholar 

  10. Mahdian, B., & Saic, S. (2010). A bibliography on blind methods for identifying image forgery. Signal Processing: Image Communication, 25(6), 389–399.

    Google Scholar 

  11. Pi, M. H., Tong, C. S., Choy, S. K., & Zhang, H. (2006). A fast and effective model for wavelet subband histograms and its application in texture image retrieval. IEEE Transactions on Image Processing, 15(10), 3078–3088.

    Article  Google Scholar 

  12. Pohjalainen, J., Rasanen, O., & Kadioglu, S. (2013). Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits. Computer Speech & Language, 29(1), 145–171.

    Article  Google Scholar 

  13. Schaefer, G., & Stich, M. (2004). UCID—An uncompressed colour image database. In Proceedings of SPIE, Storage and retrieval Methods and Applications for Multimedia, pp. 472–480.

    Google Scholar 

  14. Stamm, M., & Liu, K. J. R. (2010). Forensic detection of image manipulation using statistical intrinsic fingerprints. IEEE Transactions on Information Forensics and Security, 5(3), 492–506.

    Article  Google Scholar 

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Author information

Authors and Affiliations

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

    Gajanan K. Birajdar

  2. Department of Electronics and Telecommunication, Government Polytechnic, Nagpur, 440001, Maharashtra, India

    Vijay H. Mankar

Authors
  1. Gajanan K. Birajdar
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  2. Vijay H. Mankar
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Corresponding author

Correspondence to Gajanan K. Birajdar .

Editor information

Editors and Affiliations

  1. Dept of Comp Sci Engg, Anil Neerukonda Ins Tech & Sci, Visakhapatnam, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Sabarkantha, Sabar Institute of Technology, Sabarkantha, Gujarat, India

    Amit Joshi

  3. Studies and Management, Narsinhbhai Institute of Computer, Kadi, Gujarat, India

    Nilesh Modi

  4. Computer Studies and Management, Narsinhbhai Institute of, Kadi, Gujarat, India

    Nisarg Pathak

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© 2016 Springer Science+Business Media Singapore

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Birajdar, G.K., Mankar, V.H. (2016). Passive Image Manipulation Detection Using Wavelet Transform and Support Vector Machine Classifier. In: Satapathy, S., Joshi, A., Modi, N., Pathak, N. (eds) Proceedings of International Conference on ICT for Sustainable Development. Advances in Intelligent Systems and Computing, vol 408. Springer, Singapore. https://doi.org/10.1007/978-981-10-0129-1_47

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  • DOI: https://doi.org/10.1007/978-981-10-0129-1_47

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0127-7

  • Online ISBN: 978-981-10-0129-1

  • eBook Packages: EngineeringEngineering (R0)

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