Adaptive Camouflage Networks (ACN): A Real-Time AI-Driven Framework for Dynamic Visual Concealment in Complex Environments

Abstract

Adaptive Camouflage Networks (ACN) introduce a cyber-physical framework that couples real-time adversarial texture generation with large-area electrochromic textiles to render ground vehicles virtually invisible to CNN-based VIS-LWIR seekers. Fusing on-edge multimodal sensing (RGB, LWIR, depth) within a 256-D latent vector, a quantized StyleGAN2-LC network updates 64×64-pixel drive commands every 198 MS while consuming 2.35 W on average. Across 2.7 million software-in-the-loop frames spanning urban, rural and snowy theatres, ACN suppressed YOLOv5x detection probability from 0.82 (NATO static) to 0.19 (η² = 0.71, p < 0.001), extended median concealment horizon from 1.2 s to 5.9 s, and met < 200 MS latency in 96.7 % of trials. Event-driven refresh saved 62 % energy versus full-frame updates, enabling 4-h mission profiles without alternator upgrade. Manufacturable via roll-to-roll coating (< 50 USD m⁻²), ACN offers a near-term, retrofit-ready survivability multiplier against pervasive drone surveillance.