NoiseGuard

A tool that adds adversarial noise to protect images from AI reconstruction

COMBINED ATTACK

NoiseGuard Protection Pipeline

VAE → PGD → MIRAGE → VAE Decoder. The graphs below plot the actual values returned by the backend for every attack step.

01VAE Encode
→
02Latent PGD
→
03MIRAGE
→
04VAE Decode

1. Load and Run Combined Attack

Original

No image loaded Original image

VAE Reconstruction

Waiting for run VAE reconstruction

Protected

Waiting for run Protected image

2. Latent Statistics

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Latent Mean
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Latent Std
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Latent Shape
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Reconstruction MSE
-
Latent Distance

3. Combined Attack Curves

Each graph uses the raw loss history returned by the backend. The axes automatically scale to the observed values, so a small change is not hidden by a large empty range.

PGD latent-distance loss
MIRAGE target similarity
EVALUATION

4. Test Against an Open-Source Image Model

The backend sends the protected image to Stable Diffusion v1.5 Img2Img as a downstream reconstruction test. This is separate from the NoiseGuard attack itself.

BACKEND + OPEN-SOURCE MODEL TEST

Original

Evaluation original

NoiseGuard Protected

Evaluation protected

Stable Diffusion Reconstruction

AI reconstruction

NoiseGuard metrics

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Latent Distance
-
L∞ Perturbation
-
Budget Used
-
Protected PSNR
-
MIRAGE Score Change

Downstream model reconstruction

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Model
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Generated vs Original PSNR
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Generated vs Original MSE
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Generated vs Protected PSNR
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Generated vs Protected MSE
Status: Idle — load an image to begin