---
license: mit
language:
- en
pipeline_tag: text-classification
base_model: google/mobilebert-uncased
datasets:
- uciml/sms-spam-collection-dataset
- junioralive/india-spam-sms-classification
tags:
- sms
- spam-detection
- phishing-detection
- onnx
- cybersecurity
- india
- dlt
- mobilebert
---

# 🛡️ SilverGuard — Indian SMS Scam Detector

SilverGuard is a fine-tuned **MobileBERT** model exported to ONNX for detecting scam and phishing SMS messages in the Indian context. It outputs a continuous **threat score (0.0–1.0)** and is designed for real-time inference on mobile and edge devices via `onnxruntime`.

---

## 🧠 Model Overview

| Property | Details |
|----------|---------|
| **Base Model** | `google/mobilebert-uncased` (24M params) |
| **Task** | Binary SMS classification — Ham (0) vs Scam (1) |
| **Output** | `threat_score` — softmax scam probability \[0.0–1.0\] |
| **Export Format** | ONNX (opset 14), single self-contained file |
| **Max Sequence Length** | 128 tokens |
| **Target Deployment** | `onnxruntime`, `onnxruntime_flutter` |

---

## 📦 Files Included

| File | Description |
|------|-------------|
| `silver_guard.onnx` | Self-contained ONNX model (~90 MB) |
| `vocab.txt` | MobileBERT WordPiece vocabulary (~230 KB) |
| `model_config.json` | Inference configuration (max_length, labels, input format) |

---

## 📊 Training Data

~18,000 messages across four sources:

| Source | Type | Count |
|--------|------|-------|
| [UCI SMS Spam Collection](https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset) | Kaggle | ~5,500 |
| [India SMS Spam Classification](https://www.kaggle.com/datasets/junioralive/india-spam-sms-classification) | Kaggle | ~2,200 |
| Synthetic Indian Scam Templates | Generated | ~5,200 |
| Personal SMS (Phone Link export) | User Data | ~5,100 |

**Split:** 80% train / 10% validation / 10% test (stratified)

---

## 🏗️ Input Format — TRAI DLT Header System

All inputs follow the format:

```
HEADER [SEP] message body
```

The `HEADER` is the **TRAI DLT sender ID** or phone number — a critical signal for scam detection:

| Header Type | Example | Meaning |
|-------------|---------|---------|
| Registered DLT (bank) | `JD-SBINOT` | ✅ Legitimate transactional |
| Registered DLT (govt) | `DL-UIDAIG` | ✅ Legitimate government |
| Raw phone number | `+919876543210` | 🚨 Strong scam indicator |
| Gibberish / spoofed | `VM-URGENT`, `XX-WINNER` | 🚨 Scam indicator |

**DLT suffix convention:** `G` = Government · `T` = Transactional · `S` = Service · `P` = Promotional

If no sender ID is available (e.g. personal messages), pass the message body directly without a header.

---

## 🚨 Scam Categories Covered

The model was trained on 11 Indian scam archetypes:

- **Digital Arrest** — CBI/Police impersonation, Aadhaar fraud threats
- **Bank Freeze / KYC** — Account suspension, fake KYC update links
- **OTP Fraud** — "Share OTP to cancel/confirm transaction"
- **Link Phishing** — Fake rewards, government subsidies, shortened URLs
- **Lottery / Prize** — KBC, WhatsApp lucky draw, Google Annual Prize
- **Job Scam** — Work from home, Telegram task jobs
- **Parcel / Courier** — Fake customs duty, contraband claims
- **Insurance / Loan** — Pre-approved loan fraud, LIC bonus scams
- **Government Impersonation** — TRAI, RBI, EPFO, Income Tax fake notices
- **Investment Scam** — Crypto, Forex, guaranteed returns
- **Utility Scam** — Fake electricity/gas disconnection threats

---

## ⚙️ Usage

### Python (ONNX Runtime)

```python
import onnxruntime as ort
import numpy as np
import unicodedata
import re

# ── Minimal tokenizer (mirrors google/mobilebert-uncased exactly) ─────────────
class BertTokenizer:
    def __init__(self, vocab_file):
        with open(vocab_file, encoding="utf-8") as f:
            self.vocab = {line.rstrip("\n"): i for i, line in enumerate(f)}
        self.cls_id = self.vocab.get("[CLS]", 101)
        self.sep_id = self.vocab.get("[SEP]", 102)
        self.pad_id = self.vocab.get("[PAD]", 0)
        self.unk_id = self.vocab.get("[UNK]", 100)

    def _wordpiece(self, word):
        ids, start = [], 0
        while start < len(word):
            end, cur = len(word), None
            while start < end:
                sub = ("##" if start > 0 else "") + word[start:end]
                if sub in self.vocab:
                    cur = self.vocab[sub]; break
                end -= 1
            if cur is None:
                return [self.unk_id]
            ids.append(cur); start = end
        return ids

    def encode(self, text_a, text_b=None, max_length=128):
        text_a = unicodedata.normalize("NFD", text_a.lower())
        ids_a = [wp for tok in re.findall(r'\w+|[^\w\s]', text_a)
                 for wp in self._wordpiece(tok)]
        ids_b = None
        if text_b:
            text_b = unicodedata.normalize("NFD", text_b.lower())
            ids_b = [wp for tok in re.findall(r'\w+|[^\w\s]', text_b)
                     for wp in self._wordpiece(tok)]

        budget = max_length - (3 if ids_b else 2)
        if ids_b:
            while len(ids_a) + len(ids_b) > budget:
                (ids_a if len(ids_a) >= len(ids_b) else ids_b).pop()
        else:
            ids_a = ids_a[:budget]

        tokens = [self.cls_id] + ids_a + [self.sep_id]
        if ids_b:
            tokens += ids_b + [self.sep_id]
        mask = [1] * len(tokens)
        pad = max_length - len(tokens)
        tokens += [self.pad_id] * pad
        mask   += [0] * pad
        return tokens, mask


# ── Inference ──────────────────────────────────────────────────────────────────
session   = ort.InferenceSession("model/silver_guard.onnx")
tokenizer = BertTokenizer("model/vocab.txt")

def predict(header: str, message: str) -> float:
    """Returns threat score 0.0 (safe) → 1.0 (scam)."""
    ids, mask = tokenizer.encode(header, message) if header else tokenizer.encode(message)
    result = session.run(None, {
        "input_ids":      np.array([ids],  dtype=np.int64),
        "attention_mask": np.array([mask], dtype=np.int64),
    })
    return float(result[0][0][0])  # already softmaxed — do NOT apply softmax again

# Examples
print(predict("+919876543210", "Your Aadhaar is linked to money laundering. Call CBI now."))  # → ~1.0
print(predict("JD-SBINOT",    "Your a/c XX5678 credited with Rs 25,000 by NEFT. -SBI"))       # → ~0.0
print(predict("",             "Hey, are we meeting for dinner tonight?"))                      # → ~0.0
```

### Verdict Thresholds

| Score | Verdict |
|-------|---------|
| ≥ 0.80 | 🚨 HIGH RISK SCAM |
| ≥ 0.55 | ⚠️ LIKELY SCAM |
| ≥ 0.40 | 🟡 BORDERLINE |
| < 0.40 | ✅ SAFE (HAM) |

### Flutter / Dart

```yaml
# pubspec.yaml
flutter:
  assets:
    - assets/silver_guard.onnx
    - assets/vocab.txt
    - assets/model_config.json
dependencies:
  onnxruntime_flutter: ^1.0.0
```

```dart
final session = await OrtSession.fromAsset('assets/silver_guard.onnx');
// Combine as: "$senderHeader [SEP] $messageBody"
// Tokenize with WordPiece → pad to 128 → run session
// Output: threat_score [0.0 – 1.0]
```

---

## 🔧 ONNX Architecture

```
Input:  input_ids      [batch, 128]  int64
        attention_mask [batch, 128]  int64
           ↓
   MobileBERT Encoder (24 transformer layers)
           ↓
   Classification Head (linear → 2 logits)
           ↓
   Softmax → scam probability
           ↓
Output: threat_score   [batch, 1]    float32
```

> **Important:** The output is already softmaxed. Do **not** apply softmax again.

---

## 🏋️ Training Configuration

| Hyperparameter | Value |
|----------------|-------|
| Optimizer | AdamW |
| Learning Rate | 2e-5 |
| Batch Size | 32 |
| Max Epochs | 4 |
| Early Stopping | Patience = 2 |
| Gradient Clipping | max_norm = 1.0 |
| Warmup | 10% of total steps |
| Runtime | Google Colab T4 GPU |

---

## ⚠️ Limitations

- Optimized for **Indian SMS** — performance on non-Indian content may vary
- Scam tactics evolve rapidly; periodic retraining is recommended
- Short messages without a header (<10 tokens) may be less reliable
- Model does not analyze embedded URLs for malicious content

---

## 📄 License

MIT — free for commercial and personal use.