Abstract
Video manipulation detection plays a vital role in modern multimedia forensics. In particular, double compression detection provides significant clues leading to the video edition history and hinting at potential malevolent manipulation. While such an analysis is well-understood on images, the research on this subject remains lacking in videos and existing methods are not yet able to reliably detect double-compressed videos. This work presents a novel method for identifying double compression in H.264 codec videos. Our technique exploits the periodicity of frame residuals caused by fixed Group of Pictures in the initial compression, and employs an a contrario framework to minimize and control false detections. The proposed method can reliably detect double compression in videos. It does not require threshold tuning, thus enabling automatic detection. The code is available at https://github.com/li-yanhao/gop-detection.
| Original language | English |
|---|---|
| Title of host publication | 2023 IEEE International Conference on Image Processing, ICIP 2023, Proceedings |
| Publisher | IEEE |
| Pages | 1765-1769 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781728198354 |
| ISBN (Print) | 9781728198361 |
| DOIs | |
| Publication status | Published - 2023 |
| Externally published | Yes |
Publication series
| Name | Proceedings - International Conference on Image Processing, ICIP |
|---|---|
| ISSN (Print) | 1522-4880 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Funding
This work was supported by grants from ANR (APATE, ANR-22-CE39- 0016), Horizon Europe VERA.AI (No. 101070093), Région Île-de-France and UDOPIA (ANR-20-THIA-0013). Centre Borelli is also a member of Universite Paris Cit’e, SSA and INSERM.
Keywords
- a contrario
- deepfake detection
- group of pictures
- Video double compression
- video forensics