From Noise to Meaning: Meaningful Secret Sharing with Tamper Detection for Facial Recognition

2026-08-09

AI Summary

This work addresses the vulnerability of traditional visual secret sharing schemes, whose generated shares appear as conspicuous noise, making them susceptible to detection, collection, and tampering, while offering no integrity protection. To overcome these limitations, the authors propose a novel approach that seamlessly integrates facial image secret sharing with meaningful cover images. The method employs adaptive least significant bit steganography to embed shares and introduces a dual-layer authentication mechanism combining robust digital watermarking with cryptographic hashing to enable tamper detection and ensure data integrity. By organically merging visual secret sharing with semantically meaningful covers, the scheme effectively eliminates the suspicious appearance of shares, significantly improves recognition accuracy of reconstructed images on multiple public face datasets, and demonstrates resilience against attacks such as bit-flipping, cropping, and replacement—thereby achieving a balanced enhancement in privacy, security, and integrity.

Abstract

Popularity of AI-based face recognition system directly demands protection of sensitive biometric data used for training. Visual secret sharing is an interesting idea, as it splits facial images into secret shares that look random and spread across many institutions. However, these shares look like noise and can easily spark suspicion and recognized as encrypted content. This makes them open to targeted collection and harvest-now-decrypt-later attacks. Additionally, visual secret sharing does not detect tampering, allowing attackers to modify shares and threaten the integrity of reconstruction. In this paper, we introduce a new method that turns distracting noise-like secret shares into visually appealing cover images with additional cryptographic tamper detection. The proposed technique works with visual secret sharing and introduces cover images to embed the shares using adaptive least significant bit steganography. Here, cover images with perceptual transparency are used to store secret shares while guaranteeing complete privacy. A two layer authentication using strong digital watermarking and cryptographic hashing is used to protect the integrity of shares. The proposed technique shows high resilience in stopping bit-flipping, cropping, and substitution attacks. Extensive experiments on multiple public face datasets show that the technique shows better FR accuracy, while eliminating share conspicuousness and guaranteeing integrity. The proposed framework sets a new standard for protecting facial data in such a way that privacy, security, and integrity are protected.

Problem

• visual secret sharing
• facial recognition
• tamper detection
• biometric data protection
• steganography

Innovation

• Visual Secret Sharing
• Steganography
• Tamper Detection
• Biometric Privacy
• Digital Watermarking