IMAGECHAIN: Secure Image Ownership with Blockchain

Abstract

ImageChain is a Python application that combines image steganography, blockchain, and Interplanetary File System (IPFS) to create a secure, decentralized system for image metadata management. The system allows users to hide secret messages in images using steganography, keeping data confidential and authentic. The images are then stored on IPFS, which creates a distributed storage hash. IPFS hash and title, description metadata are stored in a local Ethereumblockchain through Solidity-written smart contracts. Updating and retrieving metadata, as well as transferring ownership among Ethereum addresses through Web3.py, are supported by the platform. Image processing is carried out through OpenCV. With these technologies together, transparency, traceability, and tamper-evident record-keeping across the life cycle of the digital image are provided. ImageChain illustrates the value of the integration of steganography, decentralized storage, and blockchain technology to create secure systems for use in digital rights management, forensic processing, and secure communication.

Introduction

1. Introduction:
ImageChain is a secure framework integrating steganography, blockchain, and IPFS (InterPlanetary File System) to ensure image integrity, traceability, ownership verification, and tamper resistance. It addresses the limitations of classic steganography (e.g., lack of provenance, access control) by incorporating Ethereum-based smart contracts and decentralized storage.

2. Core Components and Functionality:

• Steganography:
  Uses Least Significant Bit (LSB) techniques to embed metadata invisibly within an image for confidentiality.

• IPFS:
  Stores images off-chain while providing a unique content hash (CID) for each image, enabling tamper-evident storage and decentralized access.

• Blockchain (Ethereum):
  Records CIDs and metadata immutably using smart contracts, enabling ownership tracking, access control, and authenticity verification.

• Smart Contracts:
  Manage image registration, metadata validation, ownership transfer, and ensure only authorized users can modify data.

3. Methodology:

• Image Ingestion:
  Users submit images and metadata (e.g., timestamps, ownership info) for secure registration.

• Preprocessing:
  Images are checked for format and optimized for processing.

• Steganography Module:
  Metadata is embedded into image pixels using LSB.

• IPFS Module:
  Stego-image is uploaded to IPFS, returning a CID.

• Blockchain Module:
  CID and metadata are stored on Ethereum using Ganache and Truffle, ensuring immutability.

• Smart Contract Engine:
  Validates new uploads, checks metadata consistency, manages ownership rights, and allows secure updates.

• Output Stage:
  Provides confirmation, CID access, and enables later image verification or metadata queries.

4. Implementation Details:

• Command-Line Interface (CLI):
  Built in Python using Web3.py, this interface allows:

  • Embedding metadata

  • Publishing to IPFS

  • Blockchain registration

  • Image integrity checks

• Data Flow:

  • Metadata is encrypted (e.g., with AES) and steganographically embedded.

  • Stego-image is uploaded to IPFS; CID is recorded on-chain.

  • Smart contracts store metadata and enforce access control.

  • Verification retrieves CID and metadata from blockchain to validate image.

5. Key Features Demonstrated (Figures Summary):

• Secure Upload (Figs 2–3):
  Metadata is embedded, image stored in IPFS, and ownership info logged on Ethereum.

• Verification & Retrieval (Fig 4):
  Fetches metadata, downloads image, and validates CID using blockchain records.

• Ownership Transfer (Fig 5):
  Uses smart contracts to securely change ownership via authenticated transactions.

• Access Control (Figs 6–7):
  Unauthorized edits are rejected. Only the registered owner can update metadata.

• Decentralized Access (Fig 8):
  Images can be retrieved via IPFS using the CID, ensuring content availability and integrity.

• Blockchain Transparency (Fig 9):
  Ganache logs blockchain activity for traceability and debugging.

6. Use Cases and Applications:

ImageChain has wide applicability in domains that demand secure and verifiable image storage, including:

• Copyright protection

• Digital forensics

• Medical imaging

• Surveillance and law enforcement

• Decentralized image sharing

7. Related Works:

Research and existing implementations support the merging of blockchain, steganography, and IPFS in fields like:

• Medical data protection

• IoT image transmission

• Social media image verification

• Provenance tracking and forensics

Various studies have explored enhancements through deep learning, watermarking, access logs, and smart contract-based control.

Conclusion

The ImageChain project realizes a secure, decentralized, and tamper-proof system for the handling of sensitive image data through the efficient combination of cryptographic steganography, IPFS, and Ethereum smart contracts. Confidentiality, integrity, and ownership tracking are guaranteed by the concealment of encrypted data inside images and metadata in the blockchain. Local testing was made possible using Ganache to ensure successful image encoding, IPFS uploading, blockchain registration, and ownership transfer. Overall, ImageChain is a robust and scalable solution for those domains where secure image processing is needed, i.e., healthcare, legal, defense sectors. The ImageChain project also has great prospects for future development and practical employment. Transition from a local to an open Ethereum network will prove the system under practical conditions. The use of a web interface can enhance usability, and support for other multimedia types such as videos and medical images can increase its usefulness. Future development may involve improved encryption, AI-powered steganography detection, and multi-signature contracts for collaborative ownership of images. Interoperability with other blockchain systems and building a mobile app can also promote greater accessibility and adoption across health, law, and defense industries.

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Copyright

Copyright © 2025 Dr. Gousiya Begum, G Naga Sujini, Baddam Divya Sree, Rushyanjan Karthik Gudipati. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.