Each detector targets a different artifact class. The full ensemble is what makes us accurate across Midjourney, FLUX, DALL-E, Stable Diffusion, and face-swap deepfakes.
📡
FFT Frequency Analyzer
Detects GAN checkerboard patterns and diffusion sampling noise invisible to the eye.
CPU · ~5ms
🧠
ViT-Large Classifier
307M-parameter Vision Transformer fine-tuned on 1M+ real vs AI image pairs.
GPU · ~50ms
🔄
DIRE Reconstruction
Measures reconstruction error through a diffusion model — AI images live on the manifold.
GPU · ~200ms
🎯
CLIP Anomaly Score
Mahalanobis distance from real-image distribution. Generalizes to unseen generators.
GPU · ~30ms
🎚
SRM Noise Pattern
Spatial Rich Model filters extract camera sensor noise. Real photos have it, AI doesn't.
CPU · ~10ms
👤
Face Deepfake (Xception)
FaceForensics++ trained model. Activates when MediaPipe detects a face.
GPU · ~40ms
🪪
EXIF Metadata Checker
Inspects camera fingerprints and C2PA Content Credentials for provenance.
CPU · <1ms
⚡
Meta-Classifier Fusion
Logistic-regression layer learns the optimal weight for each detector — trained on labeled real vs AI data. This is what pushes accuracy above 95%.