Exposing Fake Faces Through Deep Neural Networks Combining Content and Trace Feature Extractors
Bibliographic record
Abstract
Abstract: In recent times, the proliferation of free deep learning-based software has facilitated the emergence of convincing facial swaps in videos, commonly referred to as ‘DeepFake’ (DF) videos. ‘Deep learning’ has improved the realism and accessibility of creating fake digital video content, which was previously attainable through traditional visual effects. These AIgenerated media, often referred to as DF, present a dual challenge: their creation is relatively straightforward using AI tools, yet their detection poses a significant hurdle. We address this challenge by employing Convolutional Neural Networks (CNNs) and ‘Recurrent Neural Networks’ (RNNs) to identify ‘DFs’. Specifically, our system utilizes a CNN to obtain frame level characteristics and apply them to train an RNN capable of identifying temporal inconsistencies introduced by DF creation tools. We evaluate our approach on a substantial dataset of fake videos and demonstrate competitive performance with a straightforward architecture.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".