This is a submission for the Gemma 4 Challenge: Build with Gemma 4
What I Built
SafeSMS is a privacy-first Android application designed to protect users from the rising threat of SMS-based scams, phishing, and spam.
Traditional SMS scanners and spam filters often send your private text messages to the cloud for analysis, creating severe privacy concerns. SafeSMS takes a completely different approach: it brings the intelligence directly to the device.
By running a powerful, on-device AI model, it performs real-time threat detection locally on your phone. It monitors incoming messages, categorizes them (SAFE, SUSPICIOUS, or SCAM), provides a confidence score, and explains why a message is dangerous.
The app features a sleek dark-mode UI built with Jetpack Compose, including:
- Live scanning “terminal” view
- Detailed history logs
- Visual analytics for inbox threat patterns
✨ Example Detection
Incoming SMS:
Your bank account will be blocked. Click immediately: http://bit.ly/xyz
SafeSMS Output:
- Category: SCAM
- Confidence: 92%
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Reason:
- Urgent language ("immediately")
- Suspicious shortened URL
- Impersonation of financial institution
Demo
[https://youtu.be/2NhvyiARX1c]
Code
- GitHub Repo: https://github.com/parulmalhotraiitk/safesms
- Try APK: https://github.com/parulmalhotraiitk/safesms/blob/main/release/SafeSMS.apk
How I Used Gemma 4
To enable real-time, completely private SMS analysis, SafeSMS uses Gemma 4 via LiteRT for on-device inference.
Model Selection: Gemma 4 E4B
I selected the E4B model because it perfectly fits mobile and edge environments:
Absolute Privacy
All SMS data stays on-device. No cloud calls, no data leakage.
Zero Latency & Offline Capability
Messages are analyzed instantly without any network dependency.
Resource Efficiency
The lightweight model runs efficiently inside a background Android service with minimal battery impact.
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Strong Reasoning in a Small Model
Despite its compact size, the model effectively detects:
- Phishing attempts
- Social engineering patterns
- Urgency-based scams
🧠 Prompting Strategy
The model is prompted using a structured classification + reasoning format, enabling it to return:
- Label (SAFE / SUSPICIOUS / SCAM)
- Confidence score
- Explanation
This ensures both accuracy and transparency in predictions.
⚡ Performance
- Inference Time: ~50–150 ms per SMS
- Runs fully offline
- Optimized for low battery usage
- No network permissions required (privacy-first design)
🏗️ Architecture
SafeSMS follows a fully on-device architecture, ensuring privacy, speed, and reliability.
🔄 Flow Overview
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Incoming SMS
- Captured via Android Broadcast Receiver
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Protection Service
- Background service processes SMS in real time
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SafeSMS Model Controller
- Handles preprocessing, inference, and routing
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On-Device AI Inference
- Gemma 4 E4B via LiteRT
- Classifies SMS into SAFE / SUSPICIOUS / SCAM
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Result Handling
- Stored in local SQLite database
- Sent instantly to UI
-
User Interface (Jetpack Compose)
- Live scan results
- History and analytics dashboard
🧩 Architecture Components
- SMS Layer: Broadcast Receiver
- Processing Layer: Background Protection Service
- AI Layer: LiteRT + Gemma 4
- Data Layer: SQLite (local storage)
- UI Layer: Jetpack Compose
🚀 Why This Architecture?
- 🔒 Fully private (on-device processing only)
- ⚡ Real-time detection with minimal latency
- 📡 Works completely offline
- 📊 Local analytics and history tracking
- 🧠 Efficient AI optimized for mobile devices
🔮 Future Improvements
- Multi-language scam detection
- WhatsApp and email integration
- Personalized scam pattern learning
- Federated learning (privacy-preserving improvements)
🏁 Final Thoughts
SafeSMS demonstrates how powerful AI models like Gemma 4 can run entirely on-device, enabling real-world applications that are fast, private, and reliable.
It’s a step toward a future where user data never has to leave their device to stay safe.