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🧾 Model Card — VideoMAE-DeepFake-Detector-v1 🧠 Model Overview
VideoMAE-DeepFake-Detector-v1 is a fine-tuned video deepfake detection model trained to distinguish between authentic and manipulated facial videos. The model builds upon the pretrained VideoMAE architecture and adapts it for binary classification of real versus synthetic videos.
The base model was originally trained on large-scale video action datasets, enabling strong spatiotemporal feature understanding. It was further fine-tuned on the FaceForensics++ dataset to detect visual artifacts, temporal inconsistencies, and manipulation signatures commonly found in deepfake videos.
By leveraging transformer-based video representation learning, the model captures both frame-level visual cues and motion patterns across time, allowing it to identify subtle manipulations that traditional image-based detectors may miss.
The model is designed for applications in media verification, misinformation detection, and AI-generated content monitoring.
🏗️ Training Details
Base Model: MCG-NJU/videomae-base-finetuned-kinetics
Framework: Hugging Face Transformers + PyTorch
Training Hardware: NVIDIA T4 GPU (Kaggle)
Epochs: 15
Batch Size: 4
Learning Rate: 2e-5
Optimizer: AdamW
Video Sampling: 16 frames per video clip
Resolution: 224 × 224
Training Strategy:
Transfer learning with partial freezing:
~70% of VideoMAE backbone layers frozen
Final transformer layers + classifier head fine-tuned
Dataset: FaceForensics++ (C23 compression level)
Classes:
🟢 Real Video 🔴 Deepfake Video
📊 Dataset Description
The model was trained using the FaceForensics++ dataset, a widely used benchmark for deepfake detection research.
FaceForensics++ contains manipulated videos generated using multiple facial manipulation techniques, including deepfake generation and facial reenactment.
For this model version, training used a subset consisting of:
Original videos (real)
Deepfakes manipulation videos (fake)
Each video was processed by sampling 16 frames uniformly across its duration to capture both spatial and temporal artifacts.
Label Description Real Authentic unmodified video Fake Video manipulated using deepfake synthesis techniques 🎯 Evaluation Metrics
Evaluation was performed on a held-out validation split of the dataset.
Metric Score Train Loss 0.303 Validation Loss 0.506 Accuracy 88.0% F1 Score 0.742 AUC 0.836
✅ The model demonstrates strong ability to distinguish between authentic and manipulated videos using temporal visual patterns.
💬 Example Usage import torch import numpy as np from decord import VideoReader, cpu from PIL import Image from transformers import VideoMAEForVideoClassification, VideoMAEImageProcessor
model = VideoMAEForVideoClassification.from_pretrained( "your_username/videomae-deepfake-detector" )
processor = VideoMAEImageProcessor.from_pretrained( "your_username/videomae-deepfake-detector" )
def load_video_frames(video_path, num_frames=16): vr = VideoReader(video_path, ctx=cpu(0)) total_frames = len(vr)
indices = np.linspace(0, total_frames - 1, num_frames).astype(int)
frames = vr.get_batch(indices).asnumpy()
return [Image.fromarray(f) for f in frames]
@torch.no_grad() def predict(video_path): frames = load_video_frames(video_path) inputs = processor(frames, return_tensors="pt")
outputs = model(**inputs)
probs = torch.softmax(outputs.logits, dim=1)[0]
return {
"real": float(probs[0]),
"fake": float(probs[1])
}
print(predict("sample_video.mp4"))
Output example:
{'real': 0.96, 'fake': 0.04} 🧩 Intended Use
Deepfake detection in video content Media authenticity verification AI-generated video detection pipelines Research on manipulated media detection Integration into misinformation monitoring systems
⚠️ Limitations
The model was trained on a subset of FaceForensics++ and may not generalize perfectly to unseen deepfake generation techniques.
Performance may degrade on:
heavily compressed social media videos
unseen manipulation methods
partial face occlusions
extremely short clips
This model should be used as an assistive forensic tool, not as a definitive authenticity guarantee.
🧑💻 Developer
Author: Vansh Momaya Institution: D. J. Sanghvi College of Engineering Focus Area: Computer Vision, AI Safety, Deepfake Detection, Video Understanding
Email: vanshmomaya9@gmail.com
🌍 Citation
If you use this model in research or projects:
@online{momaya2025videomaedeepfake, author = {Vansh Momaya}, title = {VideoMAE-DeepFake-Detector-v1}, year = {2025}, version = {v1}, url = {https://huggingface.co/Vansh180/VideoMae-deepfake-detector}, institution = {D. J. Sanghvi College of Engineering}, note = {Fine-tuned VideoMAE model for detecting deepfake videos using FaceForensics++}, license = {MIT} } 🚀 Acknowledgements
VideoMAE — Base architecture for video representation learning FaceForensics++ — Deepfake detection dataset benchmark Hugging Face Transformers — Training and deployment framework