A High-Fidelity Partial Face Manipulation Dataset for Enhanced Deepfake Detection
Bibliographic record
Abstract
Deepfake technology has already impacted the integrity of news and may grow to hugely destructive political and social force. The realistic and convincing nature of deepfakes poses a threat to the authenticity of information, alarming individuals and organizations. While many studies have explored the issue of deepfakes, the majority of them have focused on swapping entire faces rather than partially manipulating them, which can be more difficult to detect. In this paper, we introduce a high-fidelity partially manipulated face dataset, aiming to fill the gap in the existing deepfake research by providing a comprehensive benchmark for partially manipulated face detection. Our dataset includes a diverse set of partially manipulated faces which is generated from high-quality facial images. Our proposed alignment pipeline ensures that the partially manipulated faces may be realistically integrated into the original images, providing a more challenging evaluation environment for deepfake detection models. Both objective and subjective evaluations of our proposed dataset have shown promising results, indicating its potential to become a significant benchmark for partially manipulated face detection.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".