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A Digital Forensic Technique for Inter–Frame Video Forgery Detection Based on 3D CNN

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Information Systems Security (ICISS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11281))

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Abstract

With the present-day rapid growth in use of low-cost yet efficient video manipulating software, it has become extremely crucial to authenticate and check the integrity of digital videos, before they are used in sensitive contexts. For example, a CCTV footage acting as the primary source of evidence towards a crime scene. In this paper, we deal with a specific class of video forgery detection, viz., inter-frame forgery detection. We propose a deep learning based digital forensic technique using 3D Convolutional Neural Network (3D-CNN) for detection of the above form of video forgery. In the proposed model, we introduce a difference layer in the CNN, which mainly targets to extract the temporal information from the videos. This in turn, helps in efficient inter-frame video forgery detection, given the fact that, temporal information constitute the most suitable form of features for inter-frame anomaly detection. Our experimental results prove that the performance efficiency of the proposed deep learning 3D CNN model is \(97\%\) on an average, and is applicable to a wide range of video quality.

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Notes

  1. 1.

    A video shot is a sequence of frames, which are captured over an uninterrupted period of time, by a single video recording device.

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Acknowledgment

This work is funded by Board of Research in Nuclear Sciences (BRNS), Department of Atomic Energy (DAE), Govt. of India, Grant No. 34/20/22/2016-BRNS/34363, dated: 16/11/2016.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Rourkela, 769008, India

    Jamimamul Bakas & Ruchira Naskar

Authors
  1. Jamimamul Bakas
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  2. Ruchira Naskar
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Correspondence to Jamimamul Bakas .

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Editors and Affiliations

  1. Indian Institute of Science, Bangalore, India

    Vinod Ganapathy

  2. Pennsylvania State University, University Park, PA, USA

    Trent Jaeger

  3. Indian Institute of Technology Bombay, Mumbai, India

    R.K. Shyamasundar

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Bakas, J., Naskar, R. (2018). A Digital Forensic Technique for Inter–Frame Video Forgery Detection Based on 3D CNN . In: Ganapathy, V., Jaeger, T., Shyamasundar, R. (eds) Information Systems Security. ICISS 2018. Lecture Notes in Computer Science(), vol 11281. Springer, Cham. https://doi.org/10.1007/978-3-030-05171-6_16

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  • DOI: https://doi.org/10.1007/978-3-030-05171-6_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05170-9

  • Online ISBN: 978-3-030-05171-6

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