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SIH26150SoftwareBlockchain & Cybersecurity
₹1,50,000 Prize (₹75k Joint)
•
Deadline: 20 September 2026
•
Submitted Ideas: 0/500

Development of a Multi-Vendor DVR/NVR Forensic Analysis Tool for Standardized Acquisition, Recovery, and Analysis of Surveillance Evidence.

Ministry / Organization: National Technical Research Organisation (NTRO)
SIH Official Portal
Technical Complexity
8/10
Dataset Accessibility
7/10
36h Hackathon Viability
9/10
Step 0 • Zero-Jargon Layman Summary

“Currently, every security camera brand uses its own secret language to store video, making it impossible for police to easily view or recover footage from different systems. You are building a 'universal translator' and recovery tool that can read, fix, and analyze video evidence from any brand, ensuring it holds up in court.”

The DecodeX PS Decoder™

Deconstructed Problem Blueprint

Step 1

WHAT TO BUILD?

A unified, vendor-agnostic digital forensic software platform for surveillance evidence acquisition, recovery, and AI-driven analysis.

Step 2

WHO IS IT FOR?

Primary: NTRO, Law Enforcement Agencies, and Cyber Forensic Investigators. Secondary: Critical infrastructure security teams.

Step 3

WHY DOES IT EXIST?

Proprietary file systems and lack of industry standards create 'data silos,' leading to lost evidence, massive delays in investigations, and challenges in proving the authenticity of video in legal proceedings.

Step 4

INPUT → PROCESS → OUTPUT ARCHITECTURE PIPELINE

01 • Inputs & Telemetry

Raw hard drives/SD cards from DVR/NVRs, proprietary video files (.dav, .h264, .h265, .mp4), and metadata logs.

02 • Processing & AI Core

Automated device fingerprinting, binary file system parsing, carving for deleted data, cryptographic hashing for integrity, and AI-based object/face detection.

03 • Actionable Deliverables

Standardized video files, forensic integrity reports (PDF/JSON), event correlation timelines, and chain-of-custody logs.

Step 5

CONSTRAINTS

  • Must ensure bit-for-bit forensic integrity (no modification of original evidence).
  • Must support at least 5-6 major OEMs (Dahua, Hikvision, etc.).
  • Must operate in an offline/air-gapped environment.
  • Must generate legally admissible reports.
Step 6

JURY METRICS

  • Success rate of recovering deleted files from proprietary file systems.
  • Time taken to parse and normalize data across different vendors.
  • Accuracy of AI-based event correlation.
  • Integrity verification speed (hashing performance).
Step 7

CORE BOTTLENECK

Reverse-engineering proprietary file systems and binary headers of multiple DVR/NVR manufacturers without official documentation.

Why You Should Choose This PS (Pros)

  • High national security impact.
  • Clear, tangible deliverables.
  • Strong potential for real-world deployment by NTRO.
  • Combines cybersecurity with AI, a high-value domain.

Challenges & Risks to Watch (Cons)

  • Extremely difficult to obtain proprietary file system specifications.
  • Requires deep knowledge of low-level binary data structures.
  • Risk of 'vendor lock-out' if manufacturers change firmware.
Hackathon Blueprint

Recommended Tech Stack & Grand Finale Features

Frontend & Mobile
Electron.js (for desktop application)React.jsTailwind CSS
Backend & APIs
Python (FastAPI)C++ (for low-level binary parsing/FFmpeg integration)
AI / ML & Models
YOLOv11 for object detectionDeepSORT for trackingCustom OCR for timestamp extraction
Database & Cloud
SQLite (for local forensic logs)PostgreSQL

Killer Features to Impress the SIH Jury

One-click 'Forensic Image' creation that generates a SHA-256 hash immediately upon ingestion.
Automated 'Timeline Normalization' that syncs clocks across 5+ cameras from different brands.
AI-powered 'Smart Search' allowing investigators to query 'show me all red cars' across multiple vendor feeds.
Blockchain-based immutable audit log for chain-of-custody.
View Official SIH 2026 Portal Raw Text
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•Background Digital/Network Video Recorders (DVR/NVRs) are widely used for surveillance in government agencies, law enforcement, critical infrastructure, businesses, and residential environments. Major DVR/NVR manufacturers such as Dahua Technology, CP Plus, Honeywell Security, TP-Link, Godrej, Uniview, HIKVISON, and Matrix use proprietary storage formats, file systems, metadata structures, and video encoding mechanisms. During forensic investigations, surveillance footage serves as crucial digital evidence; however, the lack of standardization across DVR/NVR vendors makes acquisition, recovery, analysis, and validation difficult. Investigators often rely on multiple vendor-specific tools, resulting in increased investigation time, inconsistent results, timestamp synchronization issues, challenges in deleted footage recovery, and difficulties in maintaining evidence integrity. Therefore, a unified vendor-agnostic DVR/NVR forensic analysis platform is required to provide standardized workflows for evidence acquisition, recovery, analysis, validation, and reporting.
•Description The proposed solution aims to overcome challenges such as non-standard forensic acquisition methods, proprietary file systems and video formats, difficulty in recovering deleted or damaged recordings, inconsistent timestamps, limited event correlation across cameras, challenges in maintaining chain of custody, dependence on multiple tools, lack of standardized reporting, and limited use of intelligent video analytics. The tool should support major DVR/NVR OEMs including Dahua Technology, CP Plus, Honeywell Security, HIKVISON, TP-Link, Godrej, Uniview, Matrix, and other commonly used platforms. It should automatically identify DVR models, parse proprietary file systems, create forensic images, extract videos and metadata, decode proprietary formats, recover deleted footage, normalize timestamps, generate cryptographic hashes (MD5 and SHA-256), correlate events across cameras, maintain chain-of-custody records, generate reports, and perform AI-based analytics such as face, object, and motion detection. Key modules include Device Identification, Acquisition, File System & Format Parsing, Recovery, Timeline Analysis, Reporting, and Machine Learning.
•Expected Solution The expected outcome is a software-based forensic platform capable of performing standardized acquisition, recovery, analysis, validation, and reporting of surveillance evidence across multiple DVR/NVR vendors. The solution should support at least five to six major DVR/NVR OEMs (Dahua Technology, CP Plus, Honeywell Security, TP-Link, Godrej, Uniview, HIKVISON and Matrix), provide a unified forensic workflow, reduce dependency on vendor-specific tools, automate evidence acquisition and analysis, improve deleted video recovery, ensure evidence integrity and admissibility, and generate comprehensive forensic reports. Deliverables include a comparative analysis of major DVR/NVR OEMs (Dahua Technology, CP Plus, Honeywell Security, TP-Link, Godrej, Uniview, and Matrix), DVR/NVR forensic Image, system architecture documentation, a functional prototype, Standard Operating Procedures (SOPs), validation reports, user manuals, and a final project report. The tool should successfully parse proprietary file systems, decode video formats, recover deleted recordings,verify evidence integrity through cryptographic hashing, generate standardized reports, reduce analysis time, and produce reliable and legally defensible forensic results.