AI Deepfake Detector
Unmask face swaps, synthetic portraits, and manipulated video stills before they cause reputational, legal, or financial harm. Sightova dissects every pixel to expose what generative AI tries to hide.
Detection Capabilities
QUERY: SELECT * FROM deepfake_modulesFace Swap Detection
Identify stitched face regions by analyzing boundary blending artifacts, skin texture discontinuities, and warping distortions that face-swap pipelines leave behind at sub-pixel resolution.
GAN Artifact Analysis
Isolate telltale fingerprints left by generative adversarial networks — checkerboard patterns in upsampling layers, spectral irregularities, and latent space residue invisible to the naked eye.
Expression Inconsistency
Map 468 facial landmarks across temporal frames to detect micro-expression anomalies, asymmetric muscle activations, and uncanny valley patterns that synthetic faces consistently produce.
Lighting & Reflection Analysis
Reconstruct the illumination environment from specular highlights, corneal reflections, and shadow geometry to verify whether the depicted light source is physically consistent across the scene.
Frequency Domain Analysis
Apply Fourier and wavelet transforms to decompose images into frequency bands. Synthetic generators leave statistical signatures in mid-to-high frequency ranges that natural cameras never produce.
Temporal Coherence Check
Analyze frame-to-frame consistency in video stills for flickering edges, identity drift, and temporal aliasing that betrays real-time face synthesis and frame-by-frame generation pipelines.
Multi-Signal Face Forensics
A single detection method is never enough. Sightova fuses spatial, frequency, and temporal analysis into a unified confidence score — cross-referencing GAN fingerprints, lighting physics, and facial biomechanics to deliver verdicts that hold up to adversarial scrutiny.
- Ensemble scoring across 12 specialized ViT models
- Adversarial robustness against anti-detection techniques
- Real-time video frame extraction and batch analysis
"verdict": "SYNTHETIC_FACE",
"confidence": 0.987,
"face_regions": [
{
"region_id": "face_0",
"swap_detected": true,
"gan_fingerprint": "StyleGAN3",
"boundary_score": 0.94
}
],
"frequency_anomaly": true,
"lighting_consistent": false,
"expression_natural": false
}
Stop Deepfakes Before They Spread
From executive impersonation to political disinformation, synthetic faces are the fastest-growing threat vector online. Deploy Sightova to catch them at the gate.