| ---
|
| language:
|
| - en
|
| pipeline_tag: text-classification
|
| tags:
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| - sms-spam
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| - phishing-detection
|
| - scam-detection
|
| - security
|
| metrics:
|
| - f1
|
| - accuracy
|
| - precision
|
| - recall
|
| widget:
|
| - text: "Your account is blocked! Verify immediately with OTP. Send money to scam@ybl using https://scam.xyz/"
|
| example_title: "Bank KYC Scam"
|
| - text: "Congratulations! You won Rs 50,000 lottery prize. Contact urgently to claim via link: http://bit.ly/claim"
|
| example_title: "Lottery Scam"
|
| - text: "Hey, are we still meeting for lunch tomorrow at 12?"
|
| example_title: "Safe Message"
|
| ---
|
|
|
| # SCAMBERT: DistilBERT for SMS Fraud & Scam Detection
|
|
|
| SCAMBERT is a fine-tuned `distilbert-base-uncased` model specifically designed to detect social engineering, financial fraud, phishing, and scam payloads in SMS and short-form conversational text. It is built as Layer 3 of the AI Honeypot (CIPHER) Threat Intelligence Pipeline.
|
|
|
| ## Model Summary
|
|
|
| - **Model Type:** Text Classification (Binary)
|
| - **Base Model:** `distilbert-base-uncased`
|
| - **Language:** English (en)
|
| - **Task:** Spam/Scam Detection
|
| - **License:** MIT (or your project's license)
|
| - **Size:** ~255 MB
|
|
|
| ### Labels
|
|
|
| - `0`: Safe / Legitimate
|
| - `1`: Scam / Fraud / Phishing
|
|
|
| ## Performance & Metrics
|
|
|
| The model was fine-tuned on a dataset of **8,438** samples (27.5% Scam / 72.5% Safe). Due to class imbalance, class weights were applied during training.
|
|
|
| ### Calibration & Validation Results
|
|
|
| - **Best Accuracy:** 99.41%
|
| - **Best F1-Score:** 98.92%
|
| - **Calibrated Precision:** 95.08%
|
| - **Calibrated Recall:** 100.0%
|
| - **Optimal Threshold:** `0.0028` (For high-recall environments)
|
|
|
| ### Robustness Evaluation
|
|
|
| The model was tested against common bad-actor obfuscation tactics:
|
|
|
| | Tactic | Example Input | Prediction Probability | Passed |
|
| | :--- | :--- | :--- | :--- |
|
| | **URL Obfuscation** | `Win $1000 fast! Click hxxp://scammy...` | 99.9% Scam | ✅ |
|
| | **Numeric Substitution** | `W1NNER! Y0u have b33n select3d...` | 99.3% Scam | ✅ |
|
| | **Mixed Case** | `cOnGrAtUlAtIoNs, yOu WoN a FrEe...` | 89.8% Scam | ✅ |
|
|
|
| *Note: The model occasionally struggles with extremely short, contextless messages (e.g., "Call me now") as intended, relying on earlier heuristic layers for context.*
|
|
|
| ## Usage
|
|
|
| You can use this model directly with Hugging Face's `pipeline`:
|
|
|
| ```python
|
| from transformers import pipeline
|
|
|
| # Load the pipeline
|
| classifier = pipeline("text-classification", model="Digvijay05/SCAMBERT")
|
|
|
| # Inference
|
| text = "Earn Rs 5000 daily income from home part time. Click this link: http://bit.ly/job"
|
| result = classifier(text)
|
|
|
| print(result)
|
| # [{'label': 'LABEL_1', 'score': 0.99...}]
|
| ```
|
|
|
| Or run via the **Inference API**:
|
|
|
| ```python
|
| import httpx
|
|
|
| API_URL = "https://api-inference.huggingface.co/models/Digvijay05/SCAMBERT"
|
| headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}
|
|
|
| response = httpx.post(API_URL, headers=headers, json={"inputs": "Your account is locked. Verify at bit.ly/secure"})
|
| print(response.json())
|
| ```
|
|
|
| ## Deployment Considerations
|
|
|
| - **CPU Latency Estimate:** ~10-30ms / sequence
|
| - **GPU Latency Estimate:** ~2-5ms / sequence
|
| - **Recommendation:** Can be efficiently hosted on serverless CPU environments (like Render Free Tier) using Hugging Face's Inference API, or deployed natively if 512MB+ RAM is available. ONNX quantization is recommended for edge deployments.
|
|
|
| ## Intended Use
|
|
|
| This model is designed as a *semantic booster* tie-breaker layer within a multi-layered classification engine. It excels at detecting complex sentence structures, urgency, and manipulative context that standard Regex/Heuristic rules might miss.
|
|
|
