Pattern Collections & Testing

Pattern Collections

MilSpec Woodland Pattern Example
Military-grade adversarial camouflage combining traditional woodland concealment with digital AI disruption technology.

Featured pattern test below:
AI Detection Test Results
  • False face detections: 3
  • False eye detections: 0
  • False smile detections: 85
Pattern Analysis
  • Edge density: 0.1285 (628,450 edge pixels)
  • Contrast level: 92.14
  • High frequency energy: 156,892
Adversarial Effectiveness
  • False positive score: 88
  • Complexity score: 185.73
  • Overall adversarial score: 273.73
✅ EXCELLENT: High false positive rate - very confusing to AI
✅ DUAL-PURPOSE: Natural camouflage + digital disruption
✅ TACTICAL: Military-spec woodland aesthetic
Specter Styles Pattern Example
Adversarial glitch patterns designed to maximize facial detection confusion in surveillance algorithms.

Featured pattern test below:
AI Detection Test Results
  • False face detections: 2
  • False eye detections: 0
  • False smile detections: 48
Pattern Analysis
  • Edge density: 0.1463 (71,422 edge pixels)
  • Contrast level: 86.27
  • High frequency energy: 97,635
Adversarial Effectiveness
  • False positive score: 50
  • Complexity score: 120.89
  • Overall adversarial score: 170.89
✅ EXCELLENT: High false positive rate - very confusing to AI
✅ HIGH COMPLEXITY: Dense edge patterns good for disruption
Tatreez Protocol Pattern Example
Anti-surveillance clothing paying homage to Palestine.

Featured pattern test below:
AI Detection Test Results
  • False face detections: 1
  • False eye detections: 0
  • False smile detections: 200
Pattern Analysis
  • Edge density: 0.1070 (452,362 edge pixels)
  • Contrast level: 84.71
  • High frequency energy: 845,415
Adversarial Effectiveness
  • False positive score: 201
  • Complexity score: 864.59
  • Overall adversarial score: 1065.59
✅ EXCELLENT: High false positive rate - very confusing to AI
✅ HIGH COMPLEXITY: Dense edge patterns good for disruption