Deepfakes Classification of Faces Using Convolutional Neural Networks
Bibliographic record
Abstract
In the recent years, petabytes of data is being generated and uploaded online every second. To successfully detect fake contents, a deepfake detection technique is used to determine whether the uploaded content is real or fake. In this paper, a convolutional neural network-based model is proposed to detect the fake face images. The generative adversarial networks and data augmentation are used to generate the face dataset for real and fake face classification. Transfer learning techniques from pretrained deep models such as VGG16 and ResNet50 are employed in the proposed model. The proposed model is evaluated on three benchmark datasets, namely 140k Real and Fake Faces, Real and Fake Face Detection, and Fake Faces. The proposed model attained accuracies over the three datasets are 95.85%, 53.25%, and 88.63%, respectively. Moreover, to improve the obtained results of the proposed model, we combine it with other pretrained models of VGG16 and ResNet50 to construct deep ensembles. The overall performance is greatly improved with the ensemble model achieving accuracies on the three datasets as 98.79%, 75.79%, and 95.52%, respectively. Furthermore, the obtained results also show that the proposed models have superior performance than existing models.
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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 | 0.001 |
| 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 | 0.001 | 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".