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| title: ScamVerifierV3 | |
| emoji: 💻 | |
| colorFrom: pink | |
| colorTo: indigo | |
| sdk: gradio | |
| sdk_version: 5.42.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| short_description: '"Instantly detect and flag potential online scams"' | |
| # 🛡️ Scam-Signal Verifier | |
| A sophisticated AI-powered tool designed to protect students, elderly users, and everyone else from phishing attacks and fraudulent advertisements. This multi-agent system analyzes suspicious messages, URLs, and claims to provide risk assessments and actionable recommendations. | |
| ## 🎯 Problem Statement | |
| Students and elderly individuals are particularly vulnerable to: | |
| - Phishing emails and text messages | |
| - Fake seller advertisements | |
| - Investment scams | |
| - Social media fraud | |
| - Suspicious URLs and links | |
| ## 💡 Solution | |
| The Scam-Signal Verifier uses a multi-agent AI system to: | |
| 1. **Extract Claims & Information** - Parse text/URLs to identify key claims, contact info, and red flags | |
| 2. **Verify & Fact-Check** - Cross-reference claims against known scam patterns and analyze URLs | |
| 3. **Generate Human-Friendly Guidance** - Provide clear explanations and next steps | |
| ## 🤖 Multi-Agent Architecture | |
| ### Agent 1: Extractor | |
| - Parses input text and URLs | |
| - Identifies key claims and promises | |
| - Extracts contact information | |
| - Flags urgency indicators and suspicious language | |
| - Categorizes the main topic/context | |
| ### Agent 2: Verifier | |
| - Analyzes URLs for suspicious characteristics | |
| - Fact-checks claims using AI knowledge | |
| - Checks for common scam patterns | |
| - Calculates overall risk score (0-100) | |
| ### Agent 3: Explainer | |
| - Generates user-friendly explanations | |
| - Provides specific recommendations | |
| - Suggests next steps (report, block, verify) | |
| - Offers general safety tips | |
| ## 🚀 Features | |
| - **Real-time Analysis** - Instant scam detection and risk assessment | |
| - **Multi-format Support** - Analyze text messages, emails, URLs, and social media posts | |
| - **Risk Scoring** - Clear 0-100 risk score with color-coded indicators | |
| - **Detailed Explanations** - Non-technical, actionable guidance | |
| - **Technical Details** - Advanced users can view detailed analysis | |
| - **Responsive Design** - Clean, professional UI that works on all devices | |
| ## 📊 Risk Assessment Levels | |
| - 🚨 **HIGH RISK (70-100)** - Likely scam, avoid immediately | |
| - ⚠️ **MEDIUM RISK (40-69)** - Be cautious, verify before acting | |
| - ✅ **LOW RISK (0-39)** - Appears safe, but stay vigilant | |
| ## 🛠️ Technical Implementation | |
| ### Technologies Used | |
| - **Backend**: Python with OpenAI GPT-4o-mini | |
| - **Frontend**: Gradio for clean, responsive UI | |
| - **API**: OpenAI API for multi-agent reasoning | |
| - **Deployment**: Hugging Face Spaces | |
| ### Key Components | |
| - **Multi-agent orchestration** for specialized analysis tasks | |
| - **Heuristic rule engine** combined with LLM reasoning | |
| - **URL analysis** for link safety verification | |
| - **Pattern matching** for common scam indicators | |
| - **Risk scoring algorithm** based on multiple factors | |
| ## 🔧 Installation & Setup | |
| ### Prerequisites | |
| - Python 3.8+ | |
| - OpenAI API key | |
| ### Local Development | |
| ```bash | |
| # Clone the repository | |
| git clone <your-repo-url> | |
| cd scam-signal-verifier | |
| # Install dependencies | |
| pip install -r requirements.txt | |
| # Set your OpenAI API key | |
| export OPENAI_API_KEY="your-api-key-here" | |
| # Run the application | |
| python app.py | |
| ``` | |
| ### Hugging Face Deployment | |
| 1. Create a new Space on Hugging Face | |
| 2. Upload `app.py` and `requirements.txt` | |
| 3. Set `OPENAI_API_KEY` as a secret in your Space settings | |
| 4. The app will automatically deploy | |
| ## 📝 Usage Examples | |
| ### Example 1: Phishing Email | |
| **Input**: "URGENT: Your PayPal account will be suspended in 24 hours. Click here to verify: http://paypal-security-check.suspicious-domain.com" | |
| **Output**: | |
| - Risk Score: 85/100 (HIGH RISK) | |
| - Key issues: Urgency tactics, suspicious URL, impersonation | |
| - Recommendation: Block sender, report to PayPal | |
| ### Example 2: Investment Scam | |
| **Input**: "Make $5000/week working from home! No experience needed. Limited time offer! Text 'START' to 555-SCAM" | |
| **Output**: | |
| - Risk Score: 78/100 (HIGH RISK) | |
| - Key issues: Unrealistic income promises, urgency, vague details | |
| - Recommendation: Ignore and block number | |
| ### Example 3: Legitimate Message | |
| **Input**: "Hi, this is Amazon confirming your order #123456789. Your package will arrive tomorrow." | |
| **Output**: | |
| - Risk Score: 15/100 (LOW RISK) | |
| - Key issues: None significant | |
| - Recommendation: Verify order number in your Amazon account | |
| ## 🎓 Educational Value | |
| This project demonstrates: | |
| - **AI/ML Concepts**: Multi-agent systems, prompt engineering, risk assessment | |
| - **Cybersecurity**: Phishing detection, URL analysis, social engineering awareness | |
| - **Social Impact**: Protecting vulnerable populations from fraud | |
| - **Software Engineering**: Clean architecture, user experience design, API integration | |
| ## 🔒 Privacy & Security | |
| - **No Data Storage**: Messages are processed in real-time and not stored | |
| - **API Security**: OpenAI API calls are made securely with proper authentication | |
| - **Privacy First**: No personal information is collected or retained | |
| - **Open Source**: Code is transparent and auditable | |
| ## 🚀 Future Enhancements | |
| - **Multi-language Support** - Analyze scams in different languages | |
| - **Image Analysis** - Detect fraudulent images and fake screenshots | |
| - **Browser Extension** - Real-time protection while browsing | |
| - **Community Reports** - Crowdsourced scam database | |
| - **Mobile App** - Native iOS/Android applications | |
| - **Advanced ML** - Custom-trained models for scam detection | |
| ## 🤝 Contributing | |
| Contributions are welcome! This project has significant potential for impact in cybersecurity education and fraud prevention. | |
| ### Areas for Contribution: | |
| - Additional scam pattern detection | |
| - UI/UX improvements | |
| - Performance optimizations | |
| - Multi-language support | |
| - Testing and validation | |
| ## 📄 License | |
| This project is open source and available under the MIT License. | |
| ## 🙏 Acknowledgments | |
| - Built for educational purposes and social good | |
| - Designed to protect vulnerable populations from online fraud | |
| - Demonstrates practical application of AI for cybersecurity | |
| --- | |
| **⚠️ Disclaimer**: This tool provides guidance but cannot guarantee 100% accuracy. Always use your judgment and consult official sources when in doubt. If you believe you've encountered a scam, report it to local authorities and relevant platforms. | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |