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Detection of Object-Based Forgery in Surveillance Videos Utilizing Motion Residual and Deep Learning

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Distributed Computing and Intelligent Technology (ICDCIT 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13776))

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Abstract

In recent years, video surveillance plays an important role in security applications as well as legal sectors. For example, CCTV footage can be submitted as evidence of a crime scene in a courtroom. However, the recorded video footage can be manipulated, for example, an object can be removed from a video through low-cost and easily available video editing tools. Any sensitive surveillance video should be verified before being accepted as a piece of evidence of events/circumstances. In this paper, we present a motion residual and Deep Learning based forensic approach to identify object-based forged frames in surveillance videos. Firstly, the motion residuals are computed from the videos sequences to extract the video forgery footprints, which are generated due to the manipulation operations. Secondly, the computed motion residuals are fed to VGG-16 network for detecting authentic and forged frames in a surveillance video.

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Authors and Affiliations

  1. School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, 751024, India

    Mrinal Raj & Jamimamul Bakas

Authors
  1. Mrinal Raj
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  2. Jamimamul Bakas
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Correspondence to Jamimamul Bakas .

Editor information

Editors and Affiliations

  1. Indian Statistical Institute, Kolkata, India

    Anisur Rahaman Molla

  2. Kent State University, Kent, OH, USA

    Gokarna Sharma

  3. Indian Institute of Management, Lucknow, India

    Pradeep Kumar

  4. Technology Innovation Institute, Abu Dhabi, United Arab Emirates

    Sanjay Rawat

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Raj, M., Bakas, J. (2023). Detection of Object-Based Forgery in Surveillance Videos Utilizing Motion Residual and Deep Learning. In: Molla, A.R., Sharma, G., Kumar, P., Rawat, S. (eds) Distributed Computing and Intelligent Technology. ICDCIT 2023. Lecture Notes in Computer Science, vol 13776. Springer, Cham. https://doi.org/10.1007/978-3-031-24848-1_10

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  • DOI: https://doi.org/10.1007/978-3-031-24848-1_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-24847-4

  • Online ISBN: 978-3-031-24848-1

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