Veil-tiny

Veil-tiny is a 321,305-parameter visual privacy model built by Parergon.

It detects potentially sensitive regions directly from screenshots so they can be redacted locally, before the original image leaves the device.

No giant VLM. No cloud OCR round-trip. No sending the raw screenshot somewhere else just to decide what should have been hidden.

Screenshot
    ↓
Veil-tiny
    ↓
Sensitive-region mask
    ↓
Local redaction
    ↓
Sanitized screenshot

One tiny model. One job: decide what pixels should not leave your computer.

Why Veil?

Real computer-use data is messy.

Screens can contain:

emails
names
addresses
account information
credentials
API keys
customer information
passwords
private application state
personal data

Large multimodal models can reason about screenshots.

But continuously running a giant model inside a lightweight desktop recorder is expensive, slow, and unnecessary.

Veil-tiny takes the opposite approach:

Make the privacy model extremely small and extremely specialized.

Model

Property	Veil-tiny
Parameters	321,305
Input	384 × 384 RGB
Task	Binary sensitive-region segmentation
Output	Per-pixel sensitivity probability
Teacher	1,224,497 parameters
Formats	PyTorch + ONNX

Veil-tiny does not need to decide whether something is specifically an email, phone number, API key, or account ID.

It answers a narrower question:

Should these pixels be redacted?

That narrow objective is what allows the model to stay tiny.

Architecture

Veil-tiny uses a compact visual segmentation network with:

depthwise-separable convolutional blocks
lightweight encoder
top-down decoder
dense per-pixel prediction
teacher-student knowledge distillation

The deployed student contains only 321K parameters.

Training

Veil-tiny was trained from sensitive-region masks using WebPII and additional Parergon-generated desktop data.

WebPII

40,384 training screenshots
synthetic web interfaces
pixel-aligned sensitive-region annotations

Synthetic desktop data

An additional 10,000 generated desktop-style screenshots containing examples such as:

authentication screens
email-style interfaces
terminals
credentials
API keys
CRM-style interfaces
account information
dense desktop UI
safe text and hard negatives

Knowledge distillation

A 1.22M-parameter teacher supervises the 321K-parameter student alongside ground-truth masks.

Ground-truth masks ─────────────┐
                                ↓
Screenshot → Teacher → Veil-tiny
              1.22M       321K

The goal is simple:

Transfer useful visual behavior into a model small enough to run locally.

Held-out results

Evaluated on the held-out WebPII test set.

At a privacy-biased threshold of 0.10:

Metric	Result
Precision	74.70%
Recall	86.83%
F1	80.31%
Strict leak-free rate	33.32%

Threshold sweep

Threshold	Precision	Recall	F1	Leak-free
0.10	74.70%	86.83%	80.31%	33.32%
0.15	76.19%	86.24%	80.91%	31.48%
0.20	77.27%	85.80%	81.31%	29.87%
0.25	78.12%	85.42%	81.61%	28.78%
0.30	78.85%	85.08%	81.85%	27.55%
0.35	79.50%	84.75%	82.04%	26.26%
0.40	80.10%	84.44%	82.21%	24.88%
0.50	81.19%	83.82%	82.48%	22.38%

For privacy, maximizing ordinary F1 is not necessarily the best operating point.

Lower thresholds deliberately trade some precision for higher sensitive-region coverage.

What does "leak-free" mean?

We use a deliberately unforgiving metric.

A screenshot only counts as leak-free when essentially all annotated sensitive regions are covered.

Missing a visible portion of even one sensitive region can fail the entire screenshot.

A privacy model should not get credit for blurring most of your API key.

How Veil sees a screen

Veil produces a dense sensitivity probability map.

0.02 → probably safe
0.18 → suspicious
0.91 → likely sensitive

That map becomes a redaction mask.

Screenshot
    ↓
Veil-tiny
    ↓
Probability map
    ↓
Threshold
    ↓
Redaction mask

The operating point can be tuned depending on how aggressively the system should redact.

Lower threshold → more paranoid

Higher threshold → more selective

An interesting failure mode

Veil-tiny can be too paranoid on complex desktop interfaces.

On unseen applications it may incorrectly redact ordinary information-dense regions such as:

menu labels
documentation
filenames
source code
ordinary UI text

In other words, sometimes a tiny model learns:

lots of text = suspicious

instead of perfectly learning:

this particular information is private

We're publishing that limitation instead of pretending the problem is solved.

Files

`veil_tiny.pt`

PyTorch checkpoint for research and experimentation.

`veil_tiny.onnx`

Portable ONNX model for lightweight local inference.

Intended use

Veil-tiny is designed for research and experimentation around:

privacy-preserving screen recording
computer-use data
GUI agents
screenshot sanitization
multimodal datasets
local AI
edge vision
on-device privacy filtering

Important

Veil-tiny is a research model, not a privacy guarantee.

It can miss sensitive content and should not currently be used as the sole privacy or security control protecting private information.

Built small on purpose.

A visual privacy model for screen capture should be able to run where the screenshots are created.

321,305 parameters. Visual privacy before upload.

Built by Parergon.

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