How we detect cheating
Testevy does not guess. Every decision is backed by visual, behavioral, and system-level evidence. Our goal is to protect honest candidates while reliably identifying real fraud.
Action model
Each candidate receives a suspicion score based on all collected signals. We never auto-reject without evidence.
Low risk
Test accepted normally
Medium risk
Flagged for human review
High risk
Confirmed cheating
1. Visual signals
Multiple cameras and AI vision models analyze a candidate’s physical behavior to detect impersonation, device usage, and off-screen assistance.
- Multiple person detection
- Face absence or prolonged occlusion
- Identity consistency checks
- Suspicious object detection
- Dual side-camera monitoring to remove blind spots
- Abnormal gaze persistence away from test screen
- Repeated micro-glances to consistent off-screen locations
- Head-eye mismatch detection
- Cross-view contradiction detection between cameras
2. Typing signals
Human problem solving leaves behavioral fingerprints. We analyze typing dynamics to detect external assistance or automation.
- Keystroke rhythm analysis for unnatural typing bursts
- Inactivity-to-completion jumps
- Copy-like behavior without clipboard usage
3. Browser & system signals
We monitor how candidates interact with their device to detect hidden browsing, tool usage, or screen manipulation.
- Tab switching and focus loss detection
- Browser change events
- Split-screen and multi-window usage
- Fullscreen enforcement violations
- Developer tools and console access
- Abnormal refresh, navigation, and URL changes
- Cross-signal correlation with gaze and typing