Development of AI/ML based solution for detection of face-swap based deepfake videos software
Bibliographic record
Abstract
Deep learning has proven effective in a variety of tough issues, including computer vision, human-level control, and large data analytics. However, as deep learning technology advanced, software was developed that jeopardized national security, democracy, and privacy. Deepfake is a new technology that uses deep learning to create fake photos and videos that look very real. It's important to have tools that can automatically detect and check the quality of these AI- created images and videos. These systems help us quickly tell if a picture or video is real, edited, or fake, and they ensure that the quality is good and not misleading. An investigation of the strategies used to construct the most significant deepfakes, as well as the approaches proposed in the literature for detecting them. We provide a complete examination of the difficulties highlighted by deepfake technology, as well as recommendations for future and upcoming research opportunities. It also supports creating new and more reliable ways to handle deepfakes as they become more complex.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".