| | ---
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| | language:
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| | - en
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| | pipeline_tag: text-classification
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| | tags:
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| | - sms-spam
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| | - phishing-detection
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| | - scam-detection
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| | - security
|
| | metrics:
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| | - f1
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| | - accuracy
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| | - precision
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| | - recall
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| | widget:
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| | - text: "Your account is blocked! Verify immediately with OTP. Send money to scam@ybl using https://scam.xyz/"
|
| | example_title: "Bank KYC Scam"
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| | - text: "Congratulations! You won Rs 50,000 lottery prize. Contact urgently to claim via link: http://bit.ly/claim"
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| | example_title: "Lottery Scam"
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| | - text: "Hey, are we still meeting for lunch tomorrow at 12?"
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| | 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.
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| |
|
| | ## Model Summary
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| |
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| | - **Model Type:** Text Classification (Binary)
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| | - **Base Model:** `distilbert-base-uncased`
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| | - **Language:** English (en)
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| | - **Task:** Spam/Scam Detection
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| | - **License:** MIT (or your project's license)
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| | - **Size:** ~255 MB
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| |
|
| | ### Labels
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| |
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| | - `0`: Safe / Legitimate
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| | - `1`: Scam / Fraud / Phishing
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| |
|
| | ## Performance & Metrics
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| |
|
| | 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.
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| |
|
| | ### Calibration & Validation Results
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| |
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| | - **Best Accuracy:** 99.41%
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| | - **Best F1-Score:** 98.92%
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| | - **Calibrated Precision:** 95.08%
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| | - **Calibrated Recall:** 100.0%
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| | - **Optimal Threshold:** `0.0028` (For high-recall environments)
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| |
|
| | ### Robustness Evaluation
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| |
|
| | The model was tested against common bad-actor obfuscation tactics:
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| |
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| | | Tactic | Example Input | Prediction Probability | Passed |
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| | | :--- | :--- | :--- | :--- |
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| | | **URL Obfuscation** | `Win $1000 fast! Click hxxp://scammy...` | 99.9% Scam | ✅ |
|
| | | **Numeric Substitution** | `W1NNER! Y0u have b33n select3d...` | 99.3% Scam | ✅ |
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| | | **Mixed Case** | `cOnGrAtUlAtIoNs, yOu WoN a FrEe...` | 89.8% Scam | ✅ |
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| |
|
| | *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
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| |
|
| | You can use this model directly with Hugging Face's `pipeline`:
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| |
|
| | ```python
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| | from transformers import pipeline
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| |
|
| | # Load the pipeline
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| | classifier = pipeline("text-classification", model="Digvijay05/SCAMBERT")
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| |
|
| | # Inference
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| | text = "Earn Rs 5000 daily income from home part time. Click this link: http://bit.ly/job"
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| | result = classifier(text)
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| |
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| | print(result)
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| | # [{'label': 'LABEL_1', 'score': 0.99...}]
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| | ```
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| |
|
| | Or run via the **Inference API**:
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| |
|
| | ```python
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| | import httpx
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| |
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| | API_URL = "https://api-inference.huggingface.co/models/Digvijay05/SCAMBERT"
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| | headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}
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| |
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| | response = httpx.post(API_URL, headers=headers, json={"inputs": "Your account is locked. Verify at bit.ly/secure"})
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| | print(response.json())
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| | ```
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| |
|
| | ## Deployment Considerations
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| |
|
| | - **CPU Latency Estimate:** ~10-30ms / sequence
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| | - **GPU Latency Estimate:** ~2-5ms / sequence
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| | - **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
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| |
|
| | 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.
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| |
|
