ðĪ Deepfaic AI Detection Models
Welcome to our comprehensive AI Deepfake Detection Suite! Our cutting-edge models are designed to identify sophisticated manipulated content across multiple modalities, providing industry-leading accuracy and reliability.
ð Our Detection Arsenalâ
Our suite consists of five specialized models, each engineered to tackle specific types of deepfake manipulation:
Facial Deepfake Detection Modelâ
Specialized in detecting face-based manipulations and identity swaps
Our facial model excels at identifying deepfakes that alter faces, swap identities, or synthesize new facial expressions. Built to handle everything from subtle face morphing to complete identity replacement.
Key Strengths:
- ðŊ Frame-level face detection (minimum 20Ã20 pixels)
- ð Robust analysis down to 360p resolution
- ⥠Near Real-time processing capabilities
- ðŠ Detects face swaps, reenactment, and synthesis
Audio Deepfake Detection Modelâ
Advanced voice cloning and audio manipulation detection
Detects AI-generated voices, voice conversion attacks, and sophisticated audio manipulations. Perfect for identifying cloned voices, synthetic speech, and audio deepfakes.
Key Strengths:
- ðĢïļ Detects AI-generated voices and voice cloning
- âąïļ Works on short 4-second audio segments
- ⥠Ultra fast processing capabilities
- ð Analyzes both mono and stereo audio
Stable Diffusion Detection Modelâ
Next-generation visual artifact detection
Our most advanced model, specifically designed to detect content generated by diffusion models like Stable Diffusion, DALL-E, and other state-of-the-art image generators.
Key Strengths:
- ðžïļ Full-frame analysis without cropping
- ðŽ Detects low-level visual artifacts
- ð Identifies unnatural textures and patterns
Temporal Coherence Detection Modelâ
Advanced visual consistency analysis This model analyzes both fine-grained facial artifacts and global visual inconsistencies across frames to detect temporal and spatial manipulations in generated media.
Key Strengths:
- ðĨ Detects temporal inconsistencies across frames
- ðŠķ Captures fine texture inconsistencies in skin regions
- ð Detects geometric distortions in facial structure
TTS Detection Modelâ
Noise-Robust Text-To-Speech detection
This model combines high-level speech representation learning with low-level spectral signal analysis to detect subtle artifacts that occur in AI-generated voices.
Key Strengths:
- ðĢïļ Detects modern AI voice cloning and TTS synthesis
- ð§ Explainable signal-level manipulation markers
- ð Robust to noisy, real-world audio recordings