Tampering Detection in Compressed Digital Video Using Watermarking
Bibliographic record
Abstract
This paper presents a method to detect video tampering and distinguish it from common video processing operations, such as recompression, noise, and brightness increase, using a practical watermarking scheme for real-time authentication of digital video. In our method, the watermark signals represent the macroblock's and frame's indices, and are embedded into the nonzero quantized discrete cosine transform value of blocks, mostly the last nonzero values, enabling our method to detect spatial, temporal, and spatiotemporal tampering. Our method can be easily configured to adjust transparency, robustness, and capacity of the system according to the specific application at hand. In addition, our method takes advantage of content-based cryptography and increases the security of the system. While our method can be applied to any modern video codec, including the recently released high-efficiency video coding standard, we have implemented and evaluated it using the H.264/AVC codec, and we have shown that compared with the existing similar methods, which also embed extra bits inside video frames, our method causes significantly smaller video distortion, leading to a PSNR degradation of about 0.88 dB and structural similarity index decrease of 0.0090 with only 0.05% increase in bitrate, and with the bit correct rate of 0.71 to 0.88 after H.264/AVC recompression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".