Real cameras capture high frequencies naturally. AI generators produce mathematically "smooth" pixels. We use FFT and DCT to detect this unnatural lack of high-frequency energy.
Images have a baked-in Bayer filter pattern (CFA) and uniform JPEG compression. We analyze Error Level Analysis (ELA) and missing CFA Artifacts to expose splicing.
We track 468 facial landmarks across every frame to measure micro-jitters, unnatural head pose variations, and blinking anomalies that human eyes cannot detect.
We process audio through a lightweight PyTorch 2D-CNN to calculate voice spoofing probability, while measuring lip-sync desynchronization using a dual-stream SyncNet.
Real humans have a micro-pulse that changes facial skin tone slightly with every heartbeat. AI struggles to synthesize this coherent remote Photoplethysmography (rPPG) signal.
The lighting environments reflected in the left and right corneas must match perfectly. We analyze these 2D specular reflections to identify inconsistencies generated by GANs/Diffusion models.
We automatically extract and analyze the EXIF payload, detecting manipulation software signatures (Photoshop, Stable Diffusion), stripped metadata, and suspicious timestamps.
Instead of rigid thresholds, a fully trained Multi-Layer Perceptron (MLP) evaluates all 15 visual, biological, and acoustic sensors to determine an ironclad, explainable final verdict.