Source Camera Verification For Strongly Stabilized Videos
| Autor: | Husrev Taha Sencar, Enes Altinisik |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
021110 strategic defence & security studies Computer Networks and Communications business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Image and Video Processing (eess.IV) Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies Inpainting 02 engineering and technology Electrical Engineering and Systems Science - Image and Video Processing Image stabilization FOS: Electrical engineering electronic engineering information engineering Computer vision Artificial intelligence Image warping Safety Risk Reliability and Quality business Cropping |
| Popis: | Image stabilization performed during imaging and/or post-processing poses one of the most significant challenges to photo-response non-uniformity based source camera attribution from videos. When performed digitally, stabilization involves cropping, warping, and inpainting of video frames to eliminate unwanted camera motion. Hence, successful attribution requires the inversion of these transformations in a blind manner. To address this challenge, we introduce a source camera verification method for videos that takes into account the spatially variant nature of stabilization transformations and assumes a larger degree of freedom in their search. Our method identifies transformations at a sub-frame level, incorporates a number of constraints to validate their correctness, and offers computational flexibility in the search for the correct transformation. The method also adopts a holistic approach in countering disruptive effects of other video generation steps, such as video coding and downsizing, for more reliable attribution. Tests performed on one public and two custom datasets show that the proposed method is able to verify the source of 23-30% of all videos that underwent stronger stabilization, depending on computation load, without a significant impact on false attribution. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|