Detect face swaps, reenactments, and synthetic faces.
Multi-expert face analysis examines lighting, blending boundaries, temporal coherence, and biological signals to catch deepfakes that fool the human eye.
Deepfakes are getting better, fast
Face swap technology has moved from research labs to consumer apps. Anyone can generate a convincing deepfake in minutes. These are used for identity fraud in KYC workflows, fabricated evidence in legal proceedings, impersonation in financial transactions, and social engineering attacks.
Multi-signal face forensics
CheckReality doesn't rely on a single detector. We run multiple specialized models that each examine different aspects of facial imagery: boundary artifacts where a swapped face meets the original, lighting inconsistencies between face and background, biological signal analysis, and statistical patterns unique to generative models.
Where deepfake detection matters
KYC and identity verification: catch synthetic faces in selfie verification. Legal proceedings: authenticate photographic evidence and video stills. Insurance claims: detect face-swapped individuals in injury documentation. Financial services: prevent impersonation in remote account opening. HR and recruitment: verify candidates in video interviews.
What we analyze
Face Swap Detection
Identifies boundary artifacts, color mismatches, and blending inconsistencies at face-background boundaries.
Reenactment Analysis
Detects puppet-master style manipulation where facial expressions are transferred between subjects.
Lighting Consistency
Analyzes light source direction, shadow patterns, and specular reflections for physical plausibility.
GAN Fingerprinting
Identifies the unique spectral signatures left by specific generative model architectures.
Biological Signal Analysis
Examines skin texture patterns, pore distribution, and micro-expression coherence.
Synthetic Face Detection
Catches entirely AI-generated faces that don't correspond to any real person.