Scam Sleuth is a platform that allows users to report scam projects, analyze websites using AI, and for admins to review, manage, and inform the public about various scams. The website provides a secure environment for users to submit reports, check suspicious URLs, and stay informed through blog posts.
- Sign Up: New users can sign up with their email, name, and password. Their password is securely hashed, and they must verify their email before their account becomes active.
- Login: Returning users can log in using their email and password. Once authenticated, they can access features like scam reporting, URL analysis, and commenting.
- Report Form: Once logged in, users can submit reports on scam projects. They can provide details like the project name, a description, and any evidence (e.g., links, screenshots).
- Submission Review: All submitted reports are sent to the admin panel for review. The reports are private and only visible to admins until further action is taken.
- AI-Powered Analysis: Users can input a website URL to analyze how secure and trustworthy it is.
- Scoring System: The AI model evaluates the submitted URL and returns a trust score, which indicates how likely the website is to be legitimate or a scam.
- Community Feedback: After checking a URL, users can leave comments on the result page, enabling discussion and sharing additional context.
- Review Reports: Admins can access a comprehensive list of all user-submitted scam reports. They can filter these reports by date, project type, or status to prioritize review.
- Blog Management: Admins can write, edit, and publish blog posts to inform users about specific scams or scam types (e.g., phishing, investment fraud, identity theft). These blog posts are visible to the public once published.
- Viewing Blogs: Website visitors can view publicly available blog posts organized by date and category.
- Comments: Registered users can comment on blog posts to share insights, ask questions, or discuss the topic. Admins can moderate these comments before they appear publicly.
- Adding Media: Admins can enrich blog posts with images, videos, and other media types. These are securely stored and reusable, helping to enhance post quality and clarity.
- Secure User Authentication: Email verification and JWT-secured sessions.
- Scam Report Submission: Structured form for submitting detailed scam project reports.
- AI-Based URL Scanning: Input any website URL and receive a trust score generated by an AI model.
- Commenting on URLs: Community engagement through comments on each analyzed URL.
- Admin Dashboard: Comprehensive tools for managing reports, blogs, and user-generated content.
- Blog System: Informative blog posts categorized by scam type and moderated user comments.
- Frontend: Next.js
- Backend: .NET 7.0, Golang, Python
- Database: PostgreSQL, MongoDB
- Authentication: JWT
- AI/ML: Custom trust score model for URL analysis
- Email Verification: MojoAuth
This project is licensed under the MIT License. See the LICENSE file for more details.
For questions or inquiries, please contact:
- Front-End Developer Email: mahbodseyedzadegan@gmail.com
- Back-End Developers Email: arianaariaee83@gmail.com, armin890ebp@gmail.com
- GitHub: https://github.com/L000Pz/ScamSleuth