DFDS EfficientNet B2 Deepfake Detection Engine

DFDS: EfficientNet-B2 Deepfake Detection Engine.

An open-source, production-ready deepfake detection model optimized for FinTech Identity Verification (KYC) pipelines. This model leverages a two-phase transfer learning approach using EfficientNet-B2, achieving a 96.00% accuracy on in-domain verification and demonstrating high resilience against generative attacks.

This repository contains the finalized Inference Weights (pytorch_model.bin) and a plug-and-play Python Execution Pipeline (inference.py) equipped with RetinaFace bounding logic.

Ecosystem Links.

• Training Dataset: ThinothW/Deepfake-Identity-Isolated-Dataset-PreP
• Full System Architecture (GitHub): DFDS-Final-Project

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Quickstart: Running Inference.

This repository includes a standalone inference.py script that automatically handles RetinaFace mathematical cropping, Tensor Normalization, and universal hardware routing (CUDA, Apple MPS, or CPU).

1. Install Dependencies

pip install torch torchvision opencv-python retina-face pillow

2. Download and Run Download pytorch_model.bin and inference.py to your local directory.

from inference import predict_deepfake

# Pass any raw image to the pipeline
result = predict_deepfake("sample_id_photo.jpg")

print(result)
# Output: {'prediction': 'FAKE', 'fake_confidence': '98.40%', 'real_confidence': '1.60%'}

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Performance Benchmarks (Run 5).

The model was evaluated using a strict identity-isolation protocol to ensure zero data leakage between Training and Testing splits.

1. In-Domain Generalization (Primary Test Set).

Evaluated on 21,324 isolated images from the primary Training distribution.

Metric	Score
Accuracy	96.00%
ROC-AUC	99.30%
F1 Score	0.9597
Validation Loss	0.1344

Class-Level Breakdown:

• Fake (0): Precision: 0.9563 | Recall: 0.9644 | F1: 0.9603
• Real (1): Precision: 0.9638 | Recall: 0.9555 | F1: 0.9597

2. Cross-Dataset Generalization (Zero-Shot)

To test real-world resilience, the model was evaluated on 6,216 completely unseen images spanning 8 novel manipulation methods from the DF40 Benchmark.

• Overall Accuracy: 67.95%
• Overall ROC-AUC: 71.50%

Context on Architectural Generalization: It is critical to note that this is a strict zero-shot evaluation. The data proves the model successfully learns the underlying mechanics of generation families rather than simply memorizing training datasets. When exposed to entirely unseen data that shares a generative family with its training distribution (example: Diffusion-based EFS like MidJourney and CollabDiff), the model maintains a massive 83%+ accuracy.

The performance drop-off at the bottom of the table is expected, as methods like StarGAN-v2 and StyleClip represent entirely foreign generation architectures (GANs and Latent Edits) that were mathematically absent from the training distribution. Ultimately, the model is highly lethal against modern FinTech threat vectors (EFS and modern Face Swaps).

Generation Method	Type	Accuracy	F1 Score	Fake Detection %	Real Detection %
MidJourney	Diffusion / EFS	93.13%	0.9266	99.5%	86.8%
CollabDiff	Diffusion / EFS	83.13%	0.8525	68.8%	97.5%
HeyGen	Face Swap	80.00%	0.7050	51.2%	89.8%
StarGAN	GAN	74.81%	0.7831	58.7%	90.9%
DeepFaceLab	Face Swap	69.37%	0.6805	73.5%	65.2%
StarGAN-v2	GAN	52.75%	0.6730	8.2%	97.2%
StyleClip	Latent Edit	51.54%	0.6696	4.9%	98.2%
whichfaceisreal	GAN	50.00%	0.6667	0.0%	100.0%

3. Aggregated Threat Vector Performance.

Threat Category	Support (N)	Accuracy	ROC-AUC
EFS (MidJourney + CollabDiff)	1,542	87.93%	0.9830
Face Swap (HeyGen + DFL)	1,502	69.97%	0.7948
Total FinTech Relevant (EFS + FS)	3,044	79.07%	0.8901

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Technical Architecture.

• Backbone: EfficientNet-B2 (Pre-trained on ImageNet)
• Custom Head: Dropout (p=0.3) -> Linear (1408 to 2 nodes)
• Loss Function: CrossEntropyLoss
• Optimizer: AdamW
• Input Resolution: 260x260 RGB (Normalized to ImageNet mean/std)
• Face Extraction: RetinaFace (Confidence Threshold > 0.90)

Author.

Thinod Wickramasinghe · University of Plymouth · 2026

GitHub: https://github.com/thinothw

Project Supervisor - Dr. Rasika Ranaweera.