---
language:
- en
license: mit
tags:
- image-classification
- document-forgery-detection
- vit
- transformers
- forgery-detection
- document-analysis
- fyp
metrics:
- accuracy
- f1
base_model: google/vit-base-patch16-224
---

# Document Forgery Detector

A fine-tuned Vision Transformer (ViT) model for detecting forged or tampered documents.
Classifies any document image as either **real** or **forged** with 92.2% accuracy.

> This model was developed as a Final Year Project (FYP) at **Sir Syed University of Engineering & Technology (SSUET), Karachi, Pakistan**.

---

## Model Details

### Model Description

- **Model type:** Vision Transformer (ViT) fine-tuned for binary image classification
- **Base model:** `google/vit-base-patch16-224`
- **Developed by:** M. Umair Khan Computer Engineering Technology, SSUET Karachi
- **Institution:** Sir Syed University of Engineering & Technology (SSUET), Karachi, Pakistan
- **Project type:** Final Year Project (FYP)
- **Language(s):** English
- **License:** MIT
- **Finetuned from:** `google/vit-base-patch16-224`

---

## Uses

### Direct Use

This model can be used to detect whether a scanned or photographed document has been tampered with or forged.
Suitable for:
- Identity document verification (ID cards, passports)
- Academic certificate authentication
- Invoice and financial document fraud detection
- General document integrity checks

### Downstream Use

Can be integrated into document verification pipelines, KYC (Know Your Customer) systems, HR onboarding tools, or any workflow that requires document authenticity checks.

### Out-of-Scope Use

- This model is **not** designed for pixel-level forgery localization (it predicts a document-level label only)
- Not suitable for handwriting verification or signature authentication
- Should not be used as the sole verification mechanism in high-stakes legal or financial decisions without human review

---

## How to Get Started

```python
from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image, ImageChops
import torch
import torch.nn.functional as F
import io

# Load model and processor
model     = ViTForImageClassification.from_pretrained('zodumair/document-forgery-detector')
processor = ViTImageProcessor.from_pretrained('zodumair/document-forgery-detector')

def compute_ela(image_path, quality=90, scale=15):
    original = Image.open(image_path).convert('RGB')
    buf = io.BytesIO()
    original.save(buf, 'JPEG', quality=quality)
    buf.seek(0)
    recompressed = Image.open(buf).convert('RGB')
    ela = ImageChops.difference(original, recompressed)
    max_diff = max([ex[1] for ex in ela.getextrema()]) or 1
    ela = ela.point(lambda px: min(255, int(px * (255.0 / max_diff) * (scale / 10.0))))
    return ela

def predict(image_path):
    img = Image.open(image_path).convert('RGB')
    ela = compute_ela(image_path)
    blended = Image.blend(img, ela, alpha=0.3)
    inputs = processor(images=blended, return_tensors='pt')
    with torch.no_grad():
        logits = model(**inputs).logits
        probs  = F.softmax(logits, dim=-1)
        pred   = torch.argmax(probs).item()
    return {'label': model.config.id2label[pred], 'confidence': probs[0][pred].item()}

result = predict('your_document.jpg')
print(result)  # {'label': 'real', 'confidence': 0.97}
```

---

## Training Details

### Training Data

The model was trained on a combined dataset of **2000 real** and **2000 forged** document images:

- **Real documents:** Sourced from `chainyo/rvl-cdip` (RVL-CDIP dataset) — real scanned documents
  across 16 categories including invoices, letters, forms, emails, resumes, and more
- **Synthetic real documents:** Faker-generated documents (invoices, ID cards, certificates,
  passports, transcripts) rendered using PIL
- **Forged documents:** Programmatically generated by applying forgery attack functions to real
  documents, including:
  - Copy-move attack (region duplication)
  - Text replacement (erase and rewrite field values)
  - Stamp overlay (fake verification stamps)
  - JPEG compression artifacts (double-compression of regions)
  - Splicing (pasting regions from different documents)

### Preprocessing

Each image undergoes **Error Level Analysis (ELA)** blending before being passed to the model.
ELA highlights regions with inconsistent compression levels — a reliable indicator of tampering.
The ELA map is blended with the original image at `alpha=0.3` before resizing to 224x224.

### Training Hyperparameters

| Parameter | Value |
|---|---|
| Base model | google/vit-base-patch16-224 |
| Epochs | 20 (best at epoch 13) |
| Batch size | 32 |
| Learning rate | 1e-5 |
| LR scheduler | Cosine |
| Weight decay | 0.05 |
| Warmup steps | 200 |
| Label smoothing | 0.1 |
| Classifier dropout | 0.4 |
| Mixed precision | FP16 |
| Hardware | Google Colab T4 GPU |
| Training time | ~28 minutes |

---

## Model Details

- **Model type:** Vision Transformer (ViT) for image classification
- **Base model:** `google/vit-base-patch16-224`
- **Task:** Binary classification (Real vs Forged documents)
- **Developed by:** M. Umair Khan, Computer Engineering Technology
- **Institution:** SSUET Karachi, Pakistan
- **License:** MIT
- **Frameworks:** PyTorch, HuggingFace Transformers

---


  - JPEG compression artifacts
  - Region splicing


## Training Configuration

| Parameter | Value |
|---|---|
| Base model | google/vit-base-patch16-224 |
| Epochs | 15 |
| Batch size | 32 |
| Learning rate | 1e-5 |
| Scheduler | Cosine |
| Weight decay | 0.05 |
| Warmup steps | 200 |
| Label smoothing | 0.1 |
| Dropout | 0.4 |
| Precision | FP16 |
| Hardware | Google Colab T4 GPU |

---

## Evaluation Results

### Verified Test Performance (500 random samples)

| Metric | Score |
|---|---|
| Accuracy | **~91%** |
| F1 Score | **~0.91** |

> This result is based on randomized evaluation over 500 unseen test samples.

---

## Training Progress

| Epoch | Train Loss | Val Loss | Accuracy | F1 |
|---|---|---|---|---|
| 1  | 0.715 | 0.688 | 0.543 | 0.539 |
| 2  | 0.574 | 0.546 | 0.749 | 0.700 |
| 3  | 0.449 | 0.405 | 0.870 | 0.868 |
| 4  | 0.389 | 0.375 | 0.886 | 0.886 |
| 5  | 0.392 | 0.374 | 0.881 | 0.875 |
| 6  | 0.359 | 0.365 | 0.887 | 0.885 |
| 7  | 0.334 | 0.374 | 0.888 | 0.883 |
| 8  | 0.328 | 0.358 | 0.894 | 0.893 |
| 9  | 0.328 | 0.371 | 0.891 | 0.888 |
| 10 | 0.308 | 0.369 | 0.901 | 0.900 |
| 11 | 0.306 | 0.364 | 0.907 | 0.907 |
| 12 | 0.296 | 0.364 | 0.903 | 0.902 |
| 13 | 0.265 | 0.370 | 0.901 | 0.900 |
| 14 | 0.276 | 0.374 | 0.901 | 0.899 |
| 15 | 0.262 | 0.383 | 0.894 | 0.890 |

---

## Bias, Risks, and Limitations

- The forgery attacks used in training are **programmatic** — the model may not generalise perfectly
  to sophisticated AI-generated forgeries (e.g. deepfake documents, inpainting-based edits)
- Performance may vary on document types not well represented in RVL-CDIP
- The model predicts a **document-level** label only — it does not localise which region was forged
- Should be used as a screening tool, not as a definitive legal verdict

---

## Environmental Impact

- **Hardware:** Google Colab T4 GPU (NVIDIA Tesla T4, 16GB VRAM)
- **Cloud provider:** Google Colab
- **Training time:** ~28 minutes
- **Compute region:** Google Cloud (us-central1)
- Carbon emissions can be estimated using the
  [ML Impact Calculator](https://mlco2.github.io/impact#compute)

---

## Citation

If you use this model in your research or project, please cite:

```bibtex
@misc{umair2025forgerydetector,
  author    = {M. Umair Khan},
  title     = {Document Forgery Detector: A Fine-tuned ViT for Document Authenticity Classification},
  year      = {2026},
  publisher = {HuggingFace},
  institution = {Sir Syed University of Engineering & Technology, Karachi, Pakistan},
  url       = {https://huggingface.co/zodumair/document-forgery-detector}
}
```

---

## Model Card Authors

**M. Umair Khan**
Computer Engineering Technology Final Year
Sir Syed University of Engineering & Technology (SSUET), Karachi, Pakistan

---

*This model was developed as part of a Final Year Project (FYP) at SSUET Karachi.
Built using HuggingFace Transformers, PyTorch, and Google Colab.*