VeriMedia : AI-Powered Deepfake Detection Platform

Tested MVP by Jury
WorkSpace & Model Accuracy
MVP of VeriMedia

Inspiration

The growing realism of deepfake images and videos poses serious risks to digital trust, misinformation control, and personal security. Existing detection systems often fail against high-quality or unseen manipulations. This motivated us to build VeriMedia, a platform that leverages Transformer-based attention to detect subtle global and temporal inconsistencies that traditional models overlook.

What it does

VeriMedia is an AI-powered deepfake detection platform that classifies images (and extensible to videos) as Real or Fake. By combining CNN-based feature extraction with Transformer attention, it analyzes both local texture artifacts and global facial relationships to produce reliable confidence scores.

How we built it

Data Pipeline: Preprocessed benchmark deepfake datasets (FaceForensics++, Celeb-DF) using face detection, alignment, resizing, and normalization.
Model Architecture: Implemented a hybrid CNN + Transformer model, where CNNs extract local features and Transformers model long-range dependencies.
Training & Evaluation: Trained using binary cross-entropy loss and evaluated with accuracy, precision, recall, and ROC–AUC.
System Design: Designed the pipeline to be modular, enabling future extensions to video and multimodal inputs.

Challenges we ran into

Computational Overhead: Transformer models required significant memory and careful hyperparameter tuning.
Generalization Issues: Performance dropped when testing across datasets due to domain shifts.
Data Quality: High-quality deepfakes often contain minimal artifacts, making detection harder.
Overfitting: Prevented using augmentation, regularization, and early stopping.

Accomplishments that we're proud of

Successfully built a Transformer-based deepfake detection system from scratch.
Achieved strong accuracy and generalization compared to baseline CNN models.
Designed a clean, extensible architecture suitable for real-world deployment.
Gained hands-on experience with attention mechanisms and hybrid deep learning models.

What we learned

Global context is critical for deepfake detection, not just local textures.
Transformers significantly enhance robustness when paired with CNN backbones.
Dataset diversity is more important than model complexity.
Model interpretability and evaluation are essential for trust in AI systems.

What's next for VeriMedia : AI-Powered Deepfake Detection Platform

Extend detection from images to full video analysis using temporal Transformers.
Add multimodal verification (audio–visual consistency checks).
Optimize models for real-time inference.
Deploy VeriMedia as a web-based and API-driven platform for broader access.