Live-Streamed Video Reconstruction for Web Browser Forensics
Bibliographic record
Abstract
The way we use video streaming is evolving. Users used to broadcast their videos on social media platforms. These platforms enable them to interact from anywhere they want. Recently, there has been a wide range of people who use live video streaming platforms regularly. Thanks to high-speed Internet connections, live video streaming is now easier than ever. Many of these platforms broadcast live video feeds of electronic games, so young streamers use them to make money. Live streaming refers to media that is simultaneously broadcasted and recorded online in real-time. Despite the growing popularity of these platforms, there is a risk that this technology will be abused. Several other recorded cases of abuse have resulted in the emerging popularity of live streaming platforms. Many criminal and public proceedings may rely on information linked to a normal Web user's Online activity. Examining the web browser's history or cache may reveal helpful information about the suspect's activities. The evidence can reveal keys that might lead to this individual being convicted or clear. This work continues what was previously done to reconstruct cached video streams from YouTube and Twitter on Firefox. Our main aim in this paper is to examine data from a cached live stream using YouTube Gaming/Live and Nimo TV on Firefox and Chromium browsers.
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.
How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".