Real Versus Fake 4k - Authentic Resolution Assessment
Bibliographic record
Abstract
In recent years, the native 4K/Ultra High Definition (UHD) resolution has been trending towards the new normal of video content creation and distribution, but the practical pipelines of video acquisition, production and delivery often involve downscaling stages where the spatial resolution drops below the 4K level. Even though the video may be upscaled back to 4K/UHD resolution later, the content has lost its authentic resolution. This work aims at authentic resolution assessment (ARA). We first construct a database of over 10,000 real and fake 4K/UHD images. We then develop a two-stage ARA (TSARA) approach that classifies a video frame to have real or fake 4K resolution, where the first stage classifies local patches using a convolutional neural network (CNN), and the second stage aggregates local assessment into a global image level decision using logistical regression. Experimental results show that the proposed approach achieves high accuracy at low computational cost, and outperforms state-of-the-art no-reference (NR) image quality assessment (IQA) and image sharpness assessment (ISA) models. The built database and the proposed method are made publicly available1.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".