Advanced Features for Precise iFake Detection
Our comprehensive suite of AI-powered technologies delivers unmatched accuracy in identifying manipulated media.
Our Technology Stack
The AI models and methodologies powering our deepfake detection system.
Detection Architecture
Our deepfake detection system employs a multi-stage pipeline architecture that analyzes media across several dimensions:
Input Media
- Image Files
- Video Files
- YouTube URLs
Preprocessing
- Format Validation
- Frame Extraction
- Normalization
Model Analysis
- Face Detection
- Feature Extraction
- Temporal Analysis
Ensemble Classification
- Feature Fusion
- Decision Tree
- Confidence Scoring
Results
- Authentication Verdict
- Confidence Scores
- Visual Indicators
Core AI Models
Accuracy: 96% on benchmark datasets
Accuracy: 92% on FaceForensics++
Accuracy: 93% on DFDC dataset
Accuracy: 89% on video manipulation detection
Accuracy: 87% on temporal deepfake detection
Accuracy: Overall system accuracy: 94%
Training Methodology
Our models are trained on diverse datasets containing both authentic and manipulated media. The training process involves:
Supervised Learning: Using labeled datasets of known authentic and deepfake media.
Transfer Learning: Building upon pre-trained models fine-tuned for deepfake detection.
Data Augmentation: Generating variations of training data to improve model robustness.
Adversarial Training: Exposing models to adversarial examples to improve detection capabilities.
Powered by Advanced Technology
Our platform leverages cutting-edge tools and frameworks to deliver a reliable, scalable iFake detection system.
PyTorch
torchvision
timm
facenet-pytorch
retinaface-pytorch
YOLOv8-Face
PyTorch Video
LSTM/GRU
Transformers
Ultralytics
Hugging Face