Owl Detection Report & Benchmark Generator

Automated comparison of Owl Browser vs vanilla Playwright and Puppeteer on CreepJS — the industry-standard fingerprint detection tool. Run per release to verify detection and performance. Also includes a performance benchmark mode.

Results are displayed at owlbrowser.net/benchmark.

What this generates

Detection Report (default)

Screenshots and parsed fingerprint data showing:

• Playwright & Puppeteer have identical fingerprint hashes (canvas, WebGL, audio, fonts) — they leak the real device
• Owl Browser has completely different hashes per OS profile — genuine C++ source-level spoofing
• Headless detection: Playwright 100%, Puppeteer 100%, Owl Browser 0%
• GPU: Playwright/Puppeteer expose SwiftShader (dead giveaway), Owl shows real GPU profiles

Performance Benchmark (--benchmark)

Times cold start, navigation, screenshot, and full cycle for all three browsers:

• 10 sequential iterations per browser
• Statistics: min, max, avg, median, p95
• Raw timing data included for reproducibility
• Same machine, same container, same network — fair comparison

Quick Start

1. Pull the image

docker pull ghcr.io/olib-ai/owl-detection-report:latest

2. Create your .env file

cp .env.example .env
# Edit with your values

OWL_BROWSER_URL=http://your-owl-instance:8080
OWL_BROWSER_TOKEN=your-token

# Optional — S3 upload
AWS_ACCESS_KEY_ID=your-key
AWS_SECRET_ACCESS_KEY=your-secret
S3_BUCKET=your-bucket
S3_PREFIX=detection-reports

3. Run detection report

docker run --rm \
  --network host \
  --env-file .env \
  -v $(pwd)/output:/output \
  ghcr.io/olib-ai/owl-detection-report:latest

4. Run benchmark (sequential + concurrency)

docker run --rm \
  --network host \
  --env-file .env \
  -v $(pwd)/output:/output \
  ghcr.io/olib-ai/owl-detection-report:latest --benchmark

5. Run concurrency benchmark only

Keeps existing sequential data, only updates concurrency results:

docker run --rm \
  --network host \
  --env-file .env \
  -v $(pwd)/output:/output \
  ghcr.io/olib-ai/owl-detection-report:latest --concurrency

6. When to run

Run the detection report and benchmark per Owl Browser release — results only change when the browser updates. No need for daily cron.

When using S3 upload, the -v volume mount is optional since files go directly to S3.

Build from source

git clone https://github.com/Olib-AI/owl-detection-report.git
cd owl-detection-report
docker build -t owl-detection-report .

# Detection report
docker run --rm --network host --env-file .env -v $(pwd)/output:/output owl-detection-report

# Benchmark
docker run --rm --network host --env-file .env -v $(pwd)/output:/output owl-detection-report --benchmark

Environment Variables

Variable	Required	Default	Description
OWL_BROWSER_URL	Yes	—	Owl Browser REST API endpoint (e.g. http://localhost:8080)
OWL_BROWSER_TOKEN	Yes	—	Owl Browser API token
OUTPUT_DIR	No	/output	Output directory inside the container
AWS_ACCESS_KEY_ID	No	—	AWS credentials for S3 upload
AWS_SECRET_ACCESS_KEY	No	—	AWS credentials for S3 upload
S3_BUCKET	No	—	S3 bucket name
S3_PREFIX	No	detection-reports/	S3 key prefix
AWS_REGION	No	us-east-1	AWS region
CLOUDFRONT_DISTRIBUTION_ID	No	—	CloudFront distribution for cache invalidation

Output

Detection report

/output/
  report.json           # Parsed metrics + screenshot paths
  screenshots/
    playwright.webp     # Vanilla Playwright baseline
    puppeteer.webp      # Vanilla Puppeteer baseline
    owl-windows.webp    # Owl Browser with Windows profile
    owl-macos.webp      # Owl Browser with macOS profile
    owl-linux.webp      # Owl Browser with Linux profile

Benchmark

/output/
  benchmark.json        # Timing data for all three browsers

Both files are overwritten on each run — no historical data stored.

Benchmark Methodology

All three browsers are benchmarked sequentially in the same Docker container on the same machine:

1. Cold start — Playwright/Puppeteer: launch a new browser process + create page. Owl Browser: create a new context within the running engine.
2. Navigation — Navigate to https://example.com and wait for networkidle.
3. Screenshot — Capture a viewport screenshot.
4. Full cycle — Create → navigate → screenshot → close.

Each step is timed individually. 10 iterations per browser. Results include min, max, avg, median, p95, and raw timing arrays so anyone can verify.

The architectural difference: Playwright and Puppeteer launch a new OS process for each browser instance. Owl Browser creates lightweight contexts within an already-running engine — no process spawn overhead.

VPS Deployment

One-time setup

# Login to GitHub Container Registry
echo "YOUR_GITHUB_PAT" | docker login ghcr.io -u YOUR_GITHUB_USERNAME --password-stdin

# Create env file
sudo nano /etc/owl-report.env
# Paste your production config (OWL_BROWSER_URL, token, AWS creds)

# Test detection report
docker pull ghcr.io/olib-ai/owl-detection-report:latest
docker run --rm --network host --env-file /etc/owl-report.env ghcr.io/olib-ai/owl-detection-report:latest

# Test benchmark
docker run --rm --network host --env-file /etc/owl-report.env ghcr.io/olib-ai/owl-detection-report:latest --benchmark

# Run per release — no cron needed

Updating

docker pull ghcr.io/olib-ai/owl-detection-report:latest