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Deep feature based forgery detection in video using parallel convolutional neural network: VFID-Net

  • 1220: Visual and Sensory Data Processing for Real Time Intelligent Surveillance System
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Abstract

It has become easy to alter and tamper the video content flawlessly by using easily available editing software in today's era. Thus, the authenticity of media resources is at high risk. Therefore, one must verify the video to detect the originality of that video content. If the originality and authenticity of the video are compromised, it can change the viewers' perception. This work presents an automatic forgery detection tool that can help to identify the frame insertion type forgery and its location in a video. The deep features are a significant feature in recognizing the forgery and abnormal variations in the video in this work. Based on a parallel CNN model, the proposed method extracts deep features. It also calculates the distance of the correlation coefficient from the deep features, which helps to find the disassociation between the adjacent frames to identify video forgery. The VIFFD and SULFA standard datasets are used to validate the proposed method. It shows that the proposed method is beneficial in differentiating original & insertion type forged videos. The total accuracy of 99.96% achieved in frame-level forgery detection. On video-level forgery detection, 86.5% & 92% accuracy has been achieved in VIFFD & SULFA dataset, respectively. This work also helps to find the inserted frame's location, which benefits in regenerating the original video from the forged video. The proposed method is non-invasive, efficient, robust, and has low time complexity making it suitable for real applications.

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Authors and Affiliations

  1. Centre for Advanced Studies, AKTU, Lucknow, India

    Vinay Kumar & Manish Gaur

  2. Institute of Engineering & Technology, AKTU, Lucknow, India

    Vineet kansal

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  1. Vinay Kumar
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  2. Manish Gaur
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  3. Vineet kansal
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Correspondence to Vinay Kumar.

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Kumar, V., Gaur, M. & kansal, V. Deep feature based forgery detection in video using parallel convolutional neural network: VFID-Net. Multimed Tools Appl 81, 42223–42240 (2022). https://doi.org/10.1007/s11042-021-11448-0

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  • DOI: https://doi.org/10.1007/s11042-021-11448-0

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