Problem Statement
Global Security Threat: Morph attacks compromise ID verification by blending two
faces
Rising Concern: Border agencies face increasingly sophisticated fraud attempts
Technical Challenge: Traditional methods struggle with modern AI-generated morphs
Our Solution: MorphGuard
AI-Powered Detection: Identifies sophisticated morphs with 98.2% accuracy
Multi-Method Demorphing: Recovers identities using transformers and GAN inversion
Flexible Deployment: Cloud API or on-premise solutions for sensitive environments
Technology Advantage
Modern Architecture: Based on Vision Transformer (ViT) models
Comprehensive Approach: Detection, demorphing, and identity verification
Practical Implementation: Simplified attention mechanisms for morph detection
DEMO: Live detection & demorphing capabilities
DroneKit/MAVLink Integration: Streams detection events via MAVLink
New Architecture: CTM-Based Tiered Defense
Tier 1: High-Speed ViT Screening for real-time traffic analysis
Tier 2: Deep Forensic CTM Reasoning for complex morph verification
GAN-Inversion Demorphing: Pixel2Style2Pixel (pSp) framework
Identity Verification: Face enrollment and verification with InsightFace