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Texture Feature Analysis for Inter-Frame Video Tampering Detection

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Proceedings of International Joint Conference on Advances in Computational Intelligence

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

Inter-frame video forgeries can involve insertion, deletion, or duplication of frames with malicious intentions. Most of the available passive methods follow a pixel-correlation based approach that is computationally expensive as it compares each pixel of video frames to identify forgery. In this paper, a histogram-based approach is proposed that is computationally efficient and results in better classification accuracy. It computes histograms of frames having texture characteristics encoded with Local Binary Pattern (LBP). Histogram similarity of adjacent LBP coded frames is measured through the Histogram Intersection comparison metric. The differences of these adjacent metric values provide significant cues for forgery detection, that are further normalized and quantized to obtain a fixed-length feature vector. It makes the proposed approach scalable and hence enhances its applicability for variable-length videos. Training and testing are done using SVM classifier with RBF kernel. The method is capable to detect different kinds of interframe forgeries that include insertion, deletion and duplication. Due to lack of benchmark dataset of interframe video forgeries, a customized dataset is prepared through MoviePy tool that comprises total 1370 videos with interframe forgeries (frame deletion, insertion and duplication). Experimental results demonstrate an overall detection accuracy of 99% that can efficiently detect various kinds of inter-frame video forgeries. A comparative analysis with existing interframe forgery detection approaches is also presented.

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Authors and Affiliations

  1. Department of Information Technology, University Institute of Engineering and Technology, Panjab University, Chandigarh, India

    Shehnaz & Mandeep Kaur

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  1. Shehnaz
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  2. Mandeep Kaur
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Correspondence to Shehnaz .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh

    Mohammad Shorif Uddin

  2. Nazarbayev University, Nur-Sultan, Kazakhstan

    Prashant Kumar Jamwal

  3. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

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Shehnaz, Kaur, M. (2022). Texture Feature Analysis for Inter-Frame Video Tampering Detection. In: Uddin, M.S., Jamwal, P.K., Bansal, J.C. (eds) Proceedings of International Joint Conference on Advances in Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-0332-8_22

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