REAL VERSUS FAKE 4K - AUTHENTIC RESOLUTION ASSESSMENT
Bibliographic record
Abstract
IMPORTANT DETAILS ABOUT DATASET: The dataset is in the form of multiple zip files, each file containing a portion of either the training or test data for the two class labels: TRUE or FAKE. The numbers in the name of each file can be ignored. The files named "Train_True" indicate that these zip files contain the TRAINING images for the "TRUE" class label The files named "Train_Fake" indicate that these zip files contain the TRAINING images for the "FAKE" class label The files named "Test_True" indicate that these zip files contain the Test images for the "TRUE" class label The files named "Train_Fake" indicate that these zip files contain the Test images for the "FAKE" class label Once all the files have been downloaded, please ensure that you extract them into appropriate separated folders for easy analysis. 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 approach that classifies a video frame to have real or fake 4K resolution using a combination of a Convolutional Neural Network (CNN) and Logistic Regression to make the decision. 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. Database: For this purpose we have constructed a database of over 10,000 frames of images extracted from videos. The database consists of two classes of images: True 4K/UHD images and Fake 4K/UHD images. The True 4K images have been extracted from True 4K videos The Fake 4K image set consists of images which have been upscaled to 4K resolution from a lower resolution(3K, 2K, 1080p etc.)
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.011 |
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".