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ViTU-net:一种基于补丁的最低有效位方法的混合深度学习模型,用于使用混合元启发式算法进行医学图像水印和认证。

ViTU-net: A hybrid deep learning model with patch-based LSB approach for medical image watermarking and authentication using a hybrid metaheuristic algorithm.

作者信息

Nanammal V, Rajalakshmi S, Remya V, Ranjith S

机构信息

Department of Electronics and Communication Engineering, Jeppiaar Engineering College, Chennai, India.

Department of Electronics and Communication Engineering, Sri Sairam Engineering College, Chennai, India.

出版信息

Comput Biol Med. 2025 Aug;194:110393. doi: 10.1016/j.compbiomed.2025.110393. Epub 2025 Jun 2.

Abstract

In modern healthcare, telemedicine, health records, and AI-driven diagnostics depend on medical image watermarking to secure chest X-rays for pneumonia diagnosis, ensuring data integrity, confidentiality, and authenticity. A 2024 study found over 70 % of healthcare institutions faced medical image data breaches. Yet, current methods falter in imperceptibility, robustness against attacks, and deployment efficiency. ViTU-Net integrates cutting-edge techniques to address these multifaceted challenges in medical image security and analysis. The model's core component, the Vision Transformer (ViT) encoder, efficiently captures global dependencies and spatial information, while the U-Net decoder enhances image reconstruction, with both components leveraging the Adaptive Hierarchical Spatial Attention (AHSA) module for improved spatial processing. Additionally, the patch-based LSB embedding mechanism ensures focused embedding of reversible fragile watermarks within each patch of the segmented non-diagnostic region (RONI), guided dynamically by adaptive masks derived from the attention mechanism, minimizing impact on diagnostic accuracy while maximizing precision and ensuring optimal utilization of spatial information. The hybrid meta-heuristic optimization algorithm, TuniBee Fusion, dynamically optimizes watermarking parameters, striking a balance between exploration and exploitation, thereby enhancing watermarking efficiency and robustness. The incorporation of advanced cryptographic techniques, including SHA-512 hashing and AES encryption, fortifies the model's security, ensuring the authenticity and confidentiality of watermarked medical images. A PSNR value of 60.7 dB, along with an NCC value of 0.9999 and an SSIM value of 1.00, underscores its effectiveness in preserving image quality, security, and diagnostic accuracy. Robustness analysis against a spectrum of attacks validates ViTU-Net's resilience in real-world scenarios.

摘要

在现代医疗保健中,远程医疗、健康记录和人工智能驱动的诊断依赖于医学图像水印技术来保护用于肺炎诊断的胸部X光片,确保数据的完整性、保密性和真实性。2024年的一项研究发现,超过70%的医疗机构面临医学图像数据泄露问题。然而,当前的方法在不可感知性、抗攻击鲁棒性和部署效率方面存在不足。ViTU-Net集成了前沿技术,以应对医学图像安全和分析中的这些多方面挑战。该模型的核心组件,即视觉Transformer(ViT)编码器,有效地捕捉全局依赖性和空间信息,而U-Net解码器增强图像重建,这两个组件都利用自适应分层空间注意力(AHSA)模块来改进空间处理。此外,基于补丁的最低有效位嵌入机制确保在分割的非诊断区域(RONI)的每个补丁内有针对性地嵌入可逆脆弱水印,由注意力机制导出的自适应掩码动态引导,在最大限度提高精度的同时将对诊断准确性的影响降至最低,并确保空间信息的最佳利用。混合元启发式优化算法TuniBee Fusion动态优化水印参数,在探索和利用之间取得平衡,从而提高水印效率和鲁棒性。包括SHA-512哈希和AES加密在内的先进加密技术的加入,强化了模型的安全性,确保了水印医学图像的真实性和保密性。60.7dB的峰值信噪比(PSNR)值,以及0.9999的归一化互相关(NCC)值和1.00的结构相似性指数测量(SSIM)值,突出了其在保持图像质量、安全性和诊断准确性方面的有效性。针对一系列攻击的鲁棒性分析验证了ViTU-Net在现实场景中的弹性。

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