Video tampering dataset development in temporal domain for video forgery authentication

Abstract

Videos are tampered by the forgers to modify or remove their content for malicious purpose. Many video authentication algorithms are developed to detect this tampering. At present, very few standard and diversified tampered video dataset is publicly available for reliable verification and authentication of forensic algorithms. In this paper, we propose the development of total 210 videos for Temporal Domain Tampered Video Dataset (TDTVD) using Frame Deletion, Frame Duplication and Frame Insertion. Out of total 210 videos, 120 videos are developed based on Event/Object/Person (EOP) removal or modification and remaining 90 videos are created based on Smart Tampering (ST) or Multiple Tampering. 16 original videos from SULFA and 24 original videos from YouTube (VTD Dataset) are used to develop different tampered videos. EOP based videos include 40 videos for each tampering type of frame deletion, frame insertion and frame duplication. ST based tampered video contains multiple tampering in a single video. Multiple tampering is developed in three categories (1) 10-frames tampered (frame deletion, frame duplication or frame insertion) at 3-different locations (2) 20-frames tampered at 3- different locations and (3) 30-frames tampered at 3-different locations in the video. Proposed TDTVD dataset includes all temporal domain tampering and also includes multiple tampering videos. The resultant tampered videos have video length ranging from 6 s to 18 s with resolution 320X240 or 640X360 pixels. The database is comprised of static and dynamic videos with various activities, like traffic, sports, news, a ball rolling, airport, garden, highways, zoom in zoom out etc. This entire dataset is publicly accessible for researchers, and this will be especially valuable to test their algorithms on this vast dataset. The detailed ground truth information like tampering type, frames tampered, location of tampering is also given for each developed tampered video to support verifying tampering detection algorithms. The dataset is compared with state of the art and validated with two video tampering detection methods.

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Correspondence to Hitesh D. Panchal.

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Appendix

Appendix

In this section, we give summarization steps for implementation of Temporal domain tampering (EOP).

  1. 1.

    Frame Deletion (EOP)

    1. 1.

      Using MATLAB, the original input video is converted into frames

    2. 2.

      Manually frames are selected containing the event/object/person to be deleted from the original video

    3. 3.

      Start frame number del1, and end frame number del2 of the selected frames are recorded

    4. 4.

      Frames from del1 to del2 are deleted

    5. 5.

      Rest of the frames are converted into a video using MATLAB program

    6. 6.

      An actual example of frame deletion is shown in Fig. 3. In Original video named “02_original.avi”, frame number del1 = 57 to del2 = 106 (50 frames) containing the Event of “ball rolling on the table” is selected

    7. 7.

      Now to eliminate this Event, frames from del1 = 57 to del2 = 106 are deleted from the original video frames.

    8. 8.

      The remaining frames are converted into a video to develop tampered video “Tampered_EOP_02_original_framedel.avi.”

    9. 9.

      Table 2 shows all frame deleted tampered videos.

  2. 2.

    Frame Duplication (EOP)

    1. 1.

      Using MATLAB, the original input video is converted into frames

    2. 2.

      Manually frames are selected from the original video containing the event/object/person to be duplicated (copied) into the original video

    3. 3.

      Start frame number dup1, and end frame number dup2 of the selected frames are recorded

    4. 4.

      Frames from dup1 to dup2 are duplicated (copied) and pasted into an original video at another location

    5. 5.

      All frames are converted into the video using MATLAB program

    6. 6.

      An actual example of frame duplication is shown in Fig. 4. In Original video named “02_original.avi”, frame number dup1 = 55 to dup2 = 105 (51 frames) containing the Event of “ball rolling on the table” are selected

    7. 7.

      Now to duplicate this Event, frame number dup1 = 55 to dup2 = 105 are copied and pasted from frame number 145 to 195 into the original video frames

    8. 8.

      So, in frame duplicated video, this event will be seen twice

    9. 9.

      All frames are converted into a video to develop tampered video “Tampered_EOP_02_original_framedup.avi.”

    10. 10.

      Table 3 shows all frame duplicated tampered videos

  3. 3.

    Frame Insertion (EOP)

    1. 1.

      Using MATLAB, the original video and insertion video are converted into frames

    2. 2.

      Manually frames are selected from the insertion video frames containing the event/object/person to be inserted into the original video

    3. 3.

      Start frame number ins1 and end frame number ins2 of the selected frames from the insertion video frames are recorded

    4. 4.

      Frames ins1 to ins2 are inserted into original video frames

    5. 5.

      All frames are converted into a video using MATLAB program

    6. 6.

      An actual example of frame insertion is shown in Fig. 5. As seen in Fig. 5b, 70 frames (frame ins1 to ins2) from the video “04_original.avi” are selected containing the event “ball rolling on the table”

    7. 7.

      These frames from ins1 to ins2 are inserted in the original video “02_original.avi” at location starting from frame number 41

    8. 8.

      Frames are added in such a way that tampered video looks continuous

    9. 9.

      All frames are converted into a video to develop tampered video “Tampered_EOP_02_original_frameins.avi”.

    10. 10.

      Table 4 shows all frame inserted tampered videos.

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Panchal, H.D., Shah, H.B. Video tampering dataset development in temporal domain for video forgery authentication. Multimed Tools Appl (2020). https://doi.org/10.1007/s11042-020-09205-w

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Keywords

  • Frame deletion
  • Frame insertion
  • Frame duplication
  • TDTVD
  • Smart tampering
  • Multiple tampering