VeriFace: Lightweight CNN Feature Extractors for Fast Deepfake Image Authentication
Bibliographic record
Abstract
A lightweight convolutional neural network pipeline is presented in this paper for the purposes of deep fake image detection which emphasizes structured training, practical deployment and confidence estimation for real world scenarios. The method employs balanced datasets for training, standard methods for pre-processing and data augmentation of images and performance is measured in terms of accuracy, precision, recall and confusion matrix analysis to avoid overfitting and maintain generalizability across broadcasting datasets. The network architecture uses compact feature extraction and binary classification head as a binary classification system and employs probability tuning in order to provide reliable confidence scores for decision support. A web ready interface is built to allow sub second prediction of lone images via optimised pre-processing and model loading stages therefore allowing seamless end to end operation. Experimental results indicate strong classification performance attained for limited input resolution and number of epochs during training, supporting the designs made with respect to reliability, speed and deployability as misinformation reduction mechanisms.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".