onomeakpobaro/proofguard-temporal-detector

ProofGuard Temporal Detector

This model detects deepfakes in videos by analyzing temporal consistency across frames.

Model Details

• Architecture: TemporalConsistencyDetector
• Input: Video sequences (16 frames)
• Output: Binary classification (Real/Fake)

Usage

import torch

# Load the model checkpoint
checkpoint = torch.load("pytorch_model.bin", map_location="cpu")

# Access model weights
model_state_dict = checkpoint['model_state_dict']

# The model analyzes temporal patterns in video sequences
# to detect inconsistencies that indicate deepfakes

Components

• Frame feature extractor (EfficientNet backbone)
• Bidirectional LSTM for temporal modeling
• Temporal attention mechanism
• Binary classifier

Trained on diverse video datasets containing both authentic and deepfake content.

Author

Onome Akpobaro