Testing Camouflage Against the Real Adversary: an AI

Camouflage has always been graded by human eyes. But the thing hunting for you in 2026 is increasingly a detection model — so test against that. The premise Surveillance is automated now: drones, trail cameras, perimeter systems — most of what "sees" runs an object-detection network. Which makes traditional camouflage evaluation (a person squinting at a photo) the wrong test. The right test is adversarial: run the actual detector against your concealment and measure what it finds. That's the whole demo: upload a photo, and an object-detection model hunts for people in it — at four simulated distances — producing a detection-range profile and a stealth score. Simulating distance with pixels You can't move the camera after the photo is taken, but you can simulate the dominant factor in long-range detection: pixels on target . A person at 50 m simply occupies far fewer pixels than at 5 m. So each analysis run downscales the image progressively and re-runs detection: const DISTANCE_LEVELS = [ { label : ' Close (~5m) ' , scale : 1 }, { label : ' Mid (~15m) ' , scale : 0.45 }, { label : ' Far (~30m) ' , scale : 0.22 }, { label : ' Very far (~50m) ' , scale : 0.12 }, ]; for ( const level of DISTANCE_LEVELS ) { const scaled = drawScaled ( image , level . scale ); const detections = await model . detect ( scaled , 10 , 0.15 ); // best 'person' confidence at this simulated range } The output reads like a range card: detected at 5 m with 96% confidence, 41% at 15 m, invisible beyond 30 m. A stealth score aggregates it: how poorly did the adversary see you, averaged across ranges? Honest about the model The detector is COCO-SSD (a pretrained MobileNet-based model from the TensorFlow.js team) running entirely on-device — I didn't train it, and the demo says so on the page. The contribution here is the evaluation framework : using detectors as adversaries, simulating range, and turning subjective "good camo" into a measurable profile. The full version of this concept goes further