发表机构
Institute of Systems and Robotics, University of Coimbra（科英布拉大学系统与机器人研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对人脸识别生物数据保护需求，提出带篡改检测的有意义秘密共享框架，将噪声状份额转为封面图像，可抵御多种攻击，提升FR准确率并保障隐私安全与完整性。
AI 中文摘要
基于AI的人脸识别系统的普及，直接要求对其训练所用的敏感生物特征数据进行保护。视觉秘密共享是一个有趣的思路，它将人脸图像拆分为多个秘密份额，这些份额看起来是随机的，分布在多个机构中。然而，这些份额看起来像噪声，极易引发怀疑并被识别为加密内容，这使得它们易遭受针对性收集及“先收集后解密”攻击。此外，视觉秘密共享无法检测篡改，攻击者可修改份额，威胁重建的完整性。本文提出一种新方法，将易引发怀疑的噪声状秘密份额转化为具有视觉吸引力的封面图像，并附加密码学篡改检测功能。该技术与视觉秘密共享协同工作，引入封面图像，采用自适应最低有效位隐写术嵌入秘密份额。此处使用具有感知透明度的封面图像存储秘密份额，同时确保完全隐私。采用强数字水印与密码学哈希的两层认证机制，保护份额的完整性。该技术在抵御比特翻转、裁剪及替换攻击方面表现出高鲁棒性。在多个公开人脸数据集上开展的大量实验表明，该技术在消除份额显著性的同时，提升了人脸识别（FR）准确率并保证了完整性。该框架为保护人脸数据设定了新标准，实现了隐私、安全与完整性的协同保护。
英文摘要
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.
CommentsAccepted at the IEEE International Joint Conference on Biometrics (IJCB) 2026
