EDFM: An enhanced dual-branch fusion model for face deepfake detection
Bibliographic record
Abstract
Face deepfake technology brings serious security risks such as privacy leakage, false information dissemination, and network fraud, which need to be widely concerned and prevented.In recent years, many detection methods were proposed, among which enhancing the robustness and generalization ability of the model has always been an important topic.In this paper, we propose a novel enhanced dual-branch fusion model to improve the robustness and generalization ability of CNN-based face deepfake detector.Our method begins by enhancing the RGB high-frequency noise in the face image to extract its abnormal features, and then performs preservation fusion.Specifically, we use a deep separable convolution module to improve the model performance when extracting image features.When extracting noise features, we use a selective kernel module to adaptively extract more representative noise features by dynamically adjusting the convolution kernel.In addition, we specially design a multi-scale channel spatial attention fusion module to effectively fuse the feature information of each part, thereby reducing model overfitting and enhancing the robustness and generalization ability of the model.Finally, through comprehensive evaluation on several benchmark datasets, it is confirmed that our method has significantly improved robustness and generalization.
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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 | 0.000 |
| 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".