Testing Camouflage Against the Real Adversary: an AI

Source: Dev.to

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. 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. 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?

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 — a multi-model ensemble (YOLO, Faster R-CNN, RetinaNet), lighting variation, and heatmaps showing which region of you gave you away. “Evaluate against the deployed adversary, not a human proxy” generalizes: testing ad creatives against content classifiers, validating anonymization against re-identification models, red-teaming computer vision systems before someone else does. Adversarial evaluation is a product category hiding inside a security habit. Try it (images analyzed on-device, nothing uploaded): rs-03.github.io/portfolio-website/demos Source: github.com/rs-03/portfolio-website