Proofly AI Deepfake Detection Technology
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At Proofly, we have developed a state-of-the-art AI system for deepfake detection, leveraging cutting-edge deep learning techniques and rigorous scientific methodologies.
Our innovative approach ensures unparalleled accuracy, robustness, and reliability, making it a leading solution for advanced security, identity verification, and fraud detection.
}How It Works:
Fake detected
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Building a World-Class Dataset
The foundation of our model lies in a meticulously curated dataset tailored for face forgery detection:
Raw Image Collection:
High-quality real human face images were selected for model training.
Forgery Synthesis:
Forged faces were generated using advanced face-swapping techniques, creating a challenging dataset.
Multi-task Annotation:
Forged faces were generated using advanced face-swapping techniques, creating a challenging dataset.
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Dataset Construction Workflow
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Advanced Image Preprocessing
Preprocessing ensures that our model is equipped to handle real-world scenarios:
Standardization:
Images are resized and normalized to align with the architecture’s pre-training statistics.
Data Augmentation:
Random flips, rotations, and lighting variations enhance training diversity and reduce overfitting.
Random Erasing:
Introduced to simulate occlusions, improving robustness to partially obscured faces.
raining Powerful Neural Networks
We implemented and fine-tuned several architectures:
Pre-trained Models:
Leveraging advanced networks like Xception for feature-rich and accurate detection.
Custom CNNs:
Designed in-house for architectural experimentation and performance benchmarking.
These models achieved an impressive accuracy range of 94–96% on the test dataset.
Ensuring Stability and Generalization
By employing Stratified K-Fold Cross-Validation, we ensured robust performance across diverse data splits, minimizing bias and enhancing real-world applicability.
Prediction Scheme
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Prediction
Model Ensembling for Superior Results
Our ensemble approach aggregates predictions from multiple models, combining their strengths to deliver even higher precision and reliability.
Exceptional Performance Metrics
The final ensemble model achieved outstanding results:
Accuracy: 95.40%
F1 Score: 95.42%
Precision: 94.19%
Recall: 96.69%
AUC (Area Under the Curve): 99.16%
Heatmap for Test Dataset
accuracy is 0.953966070609812
f1 is 0. 9542388331814038
precision is 0.9418751124707576
recall is 0.9669314612968779
f1 is 0. 9542388331814038
precision is 0.9418751124707576
recall is 0.9669314612968779
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ROC-AUC for Models Ensemble
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applications
Proofly’s AI technology is tailored for industries where security and authenticity are critical:
Identity Verification
Fraud Detection
Critical Infrastructure Security
With its scalable design, the system seamlessly integrates into existing workflows, offering robust and reliable solutions.
Proofly’s commitment to innovation ensures that our AI models not only meet but exceed the highest standards in deepfake detection, paving the way for safer digital environments.
