Video matting tampering detection based on time and space domain traces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Deep learning-based videos usually leave imperceptible traces when tampered with. Tampered videos may be used for malicious video manipulation, which raises privacy and security concerns. Therefore the detection and localisation of tampered video traces is necessary. In this paper, we locate the tampering region by using the traces left behind by time and space domain information, and use VOS as a refinement network to improve the model performance. Firstly, the base network enhances the tampering traces by intra- and inter-frame residuals, and a dual-stream network is designed as an encoder to extract the special diagnosis from the frame residuals. Afterwards, a bidirectional convolutional LSTM and transposed convolution are embedded in the decoder to generate a prediction mask. Afterwards, a VOS network is used to obtain more accurate object boundaries. Extensive experimental results on public and synthetic manipulated datasets show that the proposed method can accurately locate tampered regions and outperforms and is robust to state-of-the-art methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it