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 — a multi-model ensemble (YOLO, Faster R-CNN, RetinaNet), lighting variation, and heatmaps showing which region of you gave you away.

Why this framing matters beyond camouflage

"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