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language: en
license: mit
tags:
- privacy
- web-tracking
- tracker-detection
- tabular-classification
- browser-fingerprinting
- safetensors
- wasm
datasets:
- olafuraron/tracker-radar-ml
metrics:
- f1
- roc_auc
- precision
- recall
---
# Tracker Classifier
A lightweight feedforward neural network for classifying third-party web
domains as tracking or non-tracking, designed for on-device inference via
WebAssembly.
## Live Preview
[Live preview](https://olafurjohannsson.github.io/tracker-ml/)
## Model Description
- **Architecture**: Feedforward NN (input -> 128 -> 64 -> 2) with ReLU and dropout
- **Size**: 181 KB (safetensors)
- **Input**: 295 behavioral and metadata features from DuckDuckGo Tracker Radar
- **Output**: Binary classification (0 = non-tracking, 1 = tracking)
- **Training data**: 12,932 domains (80% of labeled set)
- **Deployment target**: Kjarni inference engine compiled to WASM with SIMD128
## Performance (5-fold CV)
| Model | F1 | Precision | Recall | ROC-AUC |
|-------|-----|-----------|--------|---------|
| **This model (Feedforward NN)** | 0.848 +/- 0.017 | 0.804 +/- 0.037 | 0.899 +/- 0.006 | 0.928 +/- 0.008 |
| Random Forest | 0.895 +/- 0.003 | 0.895 +/- 0.006 | 0.895 +/- 0.006 | 0.958 +/- 0.002 |
| XGBoost | 0.893 +/- 0.004 | 0.887 +/- 0.006 | 0.899 +/- 0.004 | 0.959 +/- 0.002 |
| FP Heuristic (score >= 2)* | 0.355 | 0.579 | 0.257 | n/a |
*The fingerprinting heuristic targets browser API fingerprinting specifically,
not general tracking. The comparison demonstrates the gap between single-vector
and multi-vector detection.*
## Files
- `tracker_classifier.safetensors`: Model weights (181 KB)
- `config.json`: Architecture config, feature names, scaler parameters
- `scaler.joblib`: Sklearn StandardScaler for feature normalization
- `results.json`: Full evaluation metrics
## Usage
```python
import torch
import json
import numpy as np
from safetensors.torch import load_file
weights = load_file("tracker_classifier.safetensors")
config = json.load(open("config.json"))
class TrackerClassifier(torch.nn.Module):
def __init__(self, input_dim, hidden_dim=128):
super().__init__()
self.layer1 = torch.nn.Linear(input_dim, hidden_dim)
self.layer2 = torch.nn.Linear(hidden_dim, hidden_dim // 2)
self.layer3 = torch.nn.Linear(hidden_dim // 2, 2)
self.relu = torch.nn.ReLU()
def forward(self, x):
x = self.relu(self.layer1(x))
x = self.relu(self.layer2(x))
return self.layer3(x)
model = TrackerClassifier(input_dim=config["input_dim"])
model.load_state_dict(weights)
model.eval()
# Classify (standardize features first)
features = np.array([...]) # 295 features
mean = np.array(config["scaler_mean"])
scale = np.array(config["scaler_scale"])
features_scaled = (features - mean) / scale
with torch.no_grad():
logits = model(torch.FloatTensor(features_scaled).unsqueeze(0))
prediction = logits.argmax(dim=1).item()
# 0 = non-tracking, 1 = tracking
```
## On-Device Inference
This model is designed for deployment via
[Kjarni](https://github.com/olafurjohannsson/kjarni), compiled to
WebAssembly with SIMD128 acceleration. The 181 KB safetensors file and
three matrix multiplications make it suitable for real-time in-browser
classification with no data leaving the device.
## Limitations
- Trained on a point-in-time snapshot of Tracker Radar (US region)
- Metadata features (entity ownership) can cause false positives for CDN domains owned by large companies
- Requires periodic retraining as tracking techniques evolve
- Tree-based models (RF, XGBoost) outperform this model on accuracy, but cannot run in WASM
## Links
[Kjarni](https://kjarni.ai)
## Source
Code and methodology: [github.com/olafurjohannsson/tracker-ml](https://github.com/olafurjohannsson/tracker-ml) |