Approaching Deepfake Detection Models
Bibliographic record
Abstract
The combination of AI and cloud computing has led to the development of advanced techniques for manipulating audio, video, and images. Unfortunately, this has also resulted in a rise in deepfakes - manipulated media that can be incredibly convincing and difficult to detect. Thankfully, there are several countermeasures available to help combat this issue. One such measure is media confirmation, which involves verifying the authenticity of the media through various means such as metadata analysis or source verification. Another approach is media provenance, which focuses on tracing the origin of the media to determine if it has been manipulated. Finally, deepfake discovery utilizes multi-modal detection techniques that combine manual and algorithmic methods to identify manipulated media. The ultimate goal of this chapter is to automate the detection of deepfake videos using these advanced models in real-world scenarios. By doing so, we can help protect against the damaging effects that deepfakes can have on individuals and society as a whole.
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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 | 0.001 | 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 | 0.019 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".