xdfdet: explainable deepfake detection with EfficientNet-B4 and Grad-CAM
xdfdet detects manipulated face videos and shows which facial regions influence each prediction. The released aug-cutout-black detector scores 0.8981 AUC; the baseline detector scores 0.8684 on FaceForensics++. Each checkpoint used its own random test split, so read these as separate run results.
Detect a video
pip install git+https://github.com/mertkayacs/xdfdet && xdfdet predict video.mp4 --gradcam cam.png
The command downloads the default aug-cutout-black model, prints the probability that the video is real and saves a Grad-CAM heatmap. Select another detector with --model baseline. Use Python 3.10 to 3.12.
import xdfdet
model = xdfdet.load_model("aug-cutout-black")
Use the package for face detection, alignment and normalization. Each Keras model takes 12 RGB face crops, shaped (batch, 12, 224, 224, 3), and outputs real probabilities shaped (batch, 12, 1). Average the frames for a video score; below 0.5 is classified as fake.
Released detectors
| File | Augmentation and cutout | AUC |
|---|---|---|
aug-cutout-black.keras | standard augmentation, black-fill cutout | 0.8981 |
aug-cutout-random.keras | standard augmentation, random-fill cutout | 0.8820 |
aug-cutout-white.keras | standard augmentation, white-fill cutout | 0.8734 |
cutout-white.keras | white-fill cutout | 0.8700 |
baseline.keras | none | 0.8684 |
cutout-black.keras | black-fill cutout | 0.8669 |
cutout-random.keras | random-fill cutout | 0.8642 |
aug-standard.keras | standard augmentation | 0.8616 |
AUC measures how well a detector separates real and fake examples: 0.5 is chance and 1.0 is perfect. The eight weights study how augmentation and masking parts of a face during training change detection and attention. Grad-CAM shows where the model looks.
Research and limits
The UBMK 2026 paper by Mert Kaya and Venera Adanova is accepted, with no DOI yet. Its results average three runs per setting; the table above reports the released checkpoints' single runs. A ninth setting, aug-intense, is in the paper but its checkpoint was lost. Research code and full metrics | MSc thesis.
Training uses 1,000 real/fake FaceForensics++ pairs. On 398 unseen DFDC videos, AUC drops to 0.60 to 0.66, and most fakes pass as real. DFDC evaluation. Performance across age, sex and skin tone was not measured. A score alone cannot establish whether a video is authentic or justify a decision about a person.
The released weights are float32 copies of mixed-float16 training runs. For evaluation in the original GPU precision, use xdfdet.load_model(name, mixed_precision=True).
License and citation
Weights: CC BY-NC 4.0, for non-commercial research and education under the FaceForensics++ terms. Code: MIT.
@inproceedings{kaya2026augmentation,
title = {Augmentation and Cutout in Deepfake Detection: A Comparative Study of
Accuracy, Calibration, and Attention},
author = {Kaya, Mert and Adanova, Venera},
booktitle = {11th International Conference on Computer Science and Engineering (UBMK 2026)},
year = {2026},
note = {To appear}
}
@mastersthesis{kaya2025xdfdet,
title = {Explainable Deepfake Detection Using Frame Level CNN Models:
A Comparative Study of Augmentation and Cutout Techniques},
author = {Kaya, Mert},
school = {TED University},
year = {2025},
doi = {10.5281/zenodo.18998566}
}
Project and demonstrations | Kaggle weights and quickstart.
An Eschatia Labs project. Mert Kaya.