Inksight

Inksight

**Forensic signature verification with explainable AI.** Detect forged handwritten signatures and see exactly **WHERE** the forgery is.

[](https://pypi.org/project/inksight/) [](https://pypi.org/project/inksight/) [](https://opensource.org/licenses/MIT)

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Install

pip install inksight

from inksight import Inksight

model = Inksight.from_pretrained("weights/best_model.pth")
result = model.verify("reference.png", "questioned.png", explain=True)

print(result.classification)   # 'skilled_forgery'
print(result.confidence)       # 0.94
result.gradcam_heatmap         # numpy array — red = suspicious regions

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What It Does

You give it two signatures. It tells you:

1. Is it real or fake? — 4-class classification: genuine, random forgery, skilled forgery, disguised
2. WHERE exactly is the forgery? — Pixel-level visual evidence via Grad-CAM + Integrated Gradients

Feature	Other Tools	Inksight
Classification	Binary (genuine/forged)	4-class (genuine / random / skilled / disguised)
Architecture	Single CNN	Dual-backbone (ResNet-50 + Swin-T fusion)
Explainability	None	Grad-CAM + Integrated Gradients
Legal compliance	Not considered	Daubert Standard + EU AI Act ready
Install	git clone + manual setup	pip install inksight

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Architecture

Reference  ──> ResNet-50 ──> 256-d embedding ──┐
               Swin-T   ──> 256-d embedding ──┤
                                               ├──> Fusion ──> 4-class prediction
Questioned ──> ResNet-50 ──> 256-d embedding ──┤
               Swin-T   ──> 256-d embedding ──┘

**Two detectives, one verdict:**

• ResNet-50 sees pen pressure, stroke thickness, micro-details
• Swin-T sees o