This is a submission for the MLH x DEV Writing Challenge
What I Built
For this project, I built Suryakavacham — Multimodal Media Integrity & Forensic Verification Shield, a cybersecurity and media-forensics platform designed to help investigators analyse the authenticity and integrity of images, videos, and audio files.
The idea behind Suryakavacham came from a growing problem: manipulated media and deepfakes are becoming increasingly difficult to identify. A single “fake” or “real” prediction is often not enough, especially in situations where the evidence may have serious consequences.
Instead of relying on a black-box deepfake score, Suryakavacham follows an “Evidence Before Verdict” approach. It combines multiple independent forensic signals, including:
- Visual anomalies
- Audio and spectral inconsistencies
- Temporal irregularities
- Audio-video synchronization
- Metadata analysis
- Provenance verification
- Cross-modal consistency
- Cryptographic file hashing
The platform presents the result as an explainable forensic assessment rather than an absolute truth. It shows the evidence behind an assessment, the confidence level, uncertainty, contradictory signals, and recommended next steps for human reviewers.
The application includes:
- A cybersecurity-focused dashboard
- Investigation and case management
- Secure media ingestion
- Media Vault
- Visual, audio, and temporal analysis interfaces
- Evidence Explorer
- Forensic Timeline
- Evidence Fusion
- AI Forensic Reasoning
- Mitigation Center
- Forensic report generation
- Model Registry
- System Architecture visualization
- Audit trail and review workflows
Suryakavacham also includes a deterministic demonstration mode with controlled cases for synthetic video indicators, synthetic audio indicators, and cross-modal inconsistencies. Demo results are clearly labelled as CONTROLLED DEMONSTRATION so that they are not confused with real scientific measurements.
The most important lesson I learned while building this project was that AI systems used for sensitive investigations must be transparent, uncertainty-aware, and designed with human review in mind. An AI assessment should support an analyst—it should never replace evidence or make claims about a person’s intent or legal responsibility.
Demo
You can explore the deployed application here:
The demo showcases the complete investigation workflow:
- Create an investigation
- Ingest media
- Start preprocessing
- Run forensic analysis modules
- Review collected evidence
- Fuse evidence from multiple sources
- Generate an explainable assessment
- Review mitigation recommendations
- Generate a forensic report
The project repository is available here:
View the Suryakavacham Repository
Partner Technologies
I used several technologies to build Suryakavacham:
React and TypeScript
The frontend is built with React and TypeScript. TypeScript helped me create a more reliable and maintainable architecture by providing strongly typed data structures for investigations, media assets, analysis results, evidence objects, assessments, and reports.
TanStack Start and TanStack Router
TanStack Start and TanStack Router were used to create the application structure and file-based routing system. This made it easier to organise the many sections of the platform, including the dashboard, investigations, media vault, evidence explorer, reports, and system architecture pages.
Supabase and PostgreSQL
Supabase was used as the backend foundation for authentication, database operations, storage, and server-side functionality. PostgreSQL provides the data layer for investigations, media assets, analysis runs, evidence, assessments, review actions, reports, and audit events.
The application architecture is designed around authenticated access, private media storage, Row Level Security, and server-side processing.
Tailwind CSS
Tailwind CSS was used to build the visual interface. Since Suryakavacham is intended for cybersecurity professionals and digital investigators, I focused on creating an interface that is technical, professional, information-dense, and easy to navigate.
I intentionally avoided generic cyberpunk styling, excessive neon effects, and hacker clichés. Instead, the UI uses a high-trust forensic design language with clear status indicators, evidence cards, timelines, dashboards, and structured reports.
Lucide React
Lucide React was used for interface icons throughout the platform. The icons help users quickly understand actions such as uploading media, reviewing evidence, generating reports, inspecting metadata, and starting analysis.
jsPDF and JSZip
These libraries were used to support report generation and evidence export workflows. The goal is to allow investigators to create structured forensic reports and export relevant evidence in a practical format.
QRCode
QRCode functionality was included for verification-oriented workflows, such as generating records related to file hashes and forensic reports.
Working with these technologies helped me understand how frontend design, backend services, security, data modelling, and explainable AI workflows need to work together in a sensitive cybersecurity product.
Hackathon Experience
I built Suryakavacham as a project focused on the intersection of artificial intelligence, cybersecurity, media forensics, and responsible technology.
The hackathon experience gave me the opportunity to explore how a technical idea can be transformed into a complete product with a real user workflow. Instead of building only a model or a simple classifier, I wanted to create an end-to-end platform that considers investigation management, evidence collection, uncertainty, human review, mitigation, and reporting.
One of the most memorable parts of the experience was thinking about the ethical responsibility behind AI-powered media analysis. A system that detects potential manipulation should not automatically accuse people or present uncertain predictions as facts. This shaped the entire product design of Suryakavacham.
The project also helped me improve my understanding of:
- Building complex interfaces with React and TypeScript
- Designing modular analysis pipelines
- Structuring evidence and forensic results
- Creating secure media workflows
- Communicating uncertainty in AI systems
- Designing dashboards for professional users
- Separating model output from observed evidence
- Keeping human review as part of high-impact decisions
I am especially proud of the project’s central principle:
Evidence before verdict.
Suryakavacham is designed to help analysts see the signal, verify the source, and trust the evidence.
Top comments (0)