A ppt on the detection of fake emails and images.

A ppt on the detection of fake emails and images.

1. TRACE.AI AI-Powered Phishing & Deepfake Detection Presented by Team Code Catalyst

2. The Problem AI-driven phishing and deepfakes are making digital threats more convincing than ever. Phishing emails use cloned domains and real identities. Deepfakes create false visuals and voices that look authentic. Existing tools detect either emails or media, not both. Analysts waste time verifying false positives due to non-explainable alerts. Impact : Digital trust is collapsing as malicious content becomes indistinguishable from real.

3. Current tools are fragmented. Security teams juggle multiple dashboards for emails, videos, and images. Trace.ai bridges that gap. A single, explainable platform for cross-format AI threat detection. Opportunity size: Rapidly growing need for AI-native security tools that combine accuracy, explainability, and ease of use. The Opportunity

4. Our Solution Trace.ai — an AI-powered web platform that detects and explains phishing and deepfake threats in one place. Key Features: Upload or paste any email, image, audio, or video. AI analyses authenticity in seconds. Confidence score (0–100%) with clear reasoning. Quick actions: Block • Quarantine • Mark Safe. Auto-generated forensic report for investigators. Why It’s Powerful: Trace.ai builds trust through transparency — users don’t just see the result, they understand the “ why .”

5. From upload to insight under a minute. User uploads an email or media file. Dual AI pipelines analyze content: o Phishing detection o Deepfake authenticity Unified dashboard displays confidence score and evidence. User takes action (Export, Report, Quarantine How It Works Everything explained visually — fast, secure, and human-readable.

6. Tech Stack Frontend: React.js + Tailwind CSS for responsive UI and data visualization. Axios + Redux for real-time state management and API integration. Interactive dashboard with file upload, risk meter, and report view. Backend: Node.js + Express.js for REST APIs and orchestration. JWT Authentication for secure user sessions. File processing via Multer + secure storage with AWS S3.

7. AI/ML Engine (Python Microservice): Phishing Detection: Transformer-based NLP (BERT) for text + header analysis. Deepfake Detection: CNN + transformer hybrid for facial and voice anomalies. Flask/ FastAPI service connected to Node.js backend via REST API. Database & Deployment: MongoDB Atlas for logs, scores, and user data. Dockerized microservices on cloud (Render / AWS). CI/CD ready for scalability. Why It Matters: A practical, end-to-end AI + web architecture that can scale from demo → real product.

8. Unified Detection: Phishing + Deepfake in one tool (rarely done together) Explainable AI: Every decision backed by visible clues User-Centric Design: Built for both analysts & regular users Cross-Format Analysis: Emails, media, and voice — same dashboard Scalable Tech Base: MERN + AI microservice integration for performance 1. 2 . 3 . 4 . 5 . Why Trace.AI Stands Out Trace.ai = Where AI meets Human Trust.

9. Our Team Harsh Nagori — MERN Stack Developer Frontend + Backend integration, secure API handling, and deployment. Arriyaan Ali Syed — UI/UX + Frontend Developer Dashboard design, user journey, and React visualization. Naman Yadav — AI/ML Developer Phishing detection (NLP) and Deepfake model development. Together, we bridge design, engineering, and AI into one cohesive, build-ready product.

10. Next Steps: Add real-time browser extension for email scanning. Improve detection accuracy with diverse datasets. Integrate SOC (Security Operations Centre) workflow API. Ask: GPU credits for media model optimization. Mentorship on scaling AI inference for production. Support for deploying a public beta Future Scope & Ask

11. Vision To restore digital trust — one trace at a time. Trace the truth. Defend the digital world. TRACE.AI AI-Powered Phishing & Deepfake Detection

12. YOU THANK