AI-native analysis
Uses deep learning models trained on research-grade, diverse datasets to detect subtle manipulation, micro-expression, and inconsistencies across audio, video and image.
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Cyberette’s technology stack delivers a multi-layered approach to fraud detection that gives analysts more than a simple detection result, providing documented indicators, metadata traces, and reproducible analysis records that support expert review, investigative workflows, and high-stakes decision-making.
Cyberette’s multi-purpose fraud detection uses 6 different detection methods to ensure no AI or deception threat goes undetected.
Detects inconsistencies in facial features, expressions, or unnatural face-to-background alignment.
Aligns and compares audio and video streams to expose manipulation.
Tracks subtle human signals to assess liveness and deceptive behavior.
Examines files at the pixel and signal level to reveal synthetic traces.
Verifies file authenticity and origin using metadata and provenance standards (C2PA) to expose irregularities or tampering.
Monitors user and communication behavior to identify unusual patterns that may indicate manipulation or fraudulent activity.
Cyberette provides detailed detection across images, videos, and audio ensuring no deception goes unnoticed.
Analyse images with forensic precision to uncover manipulated or AI-generated content.
Detects faces and assesses whether they appear genuine or spoofed.
Identifies anomalies that may indicate manipulation or face swaps.
Detects fully synthetic or partially edited images and shows what was changed.
Flags possible background replacement.
Checks for signs of image enhancements.
Analyzes video frame by frame, linking indicators across frames to detect manipulation.
Examines video frame by frame, supported by timestamped anomaly logs
Measures alignment between audio and mouth movements, providing with flagged segments and visual indicators.
Evaluates motion patterns and human-like gestures to identify robotic, unnatural or suspicious behavior.
Detects primary emotions throughout the video and builds a micro expression map.
Inspects audio to detect voice manipulation, AI-generated speech and other synthetic alterations.
Identifies signs of cloned voices, with likelihood scores and timestamps.
Flags potentially synthesized, converted, or text-to-speech audio segments.
Identifies speakers and highlights inconsistencies when present.
Detects edits or tampering in background sounds.
Flags cut, rearranged, or edited segments.
Scores anomalies in tone, pitch, and stability.
Supports detection across multiple languages and accents.