【摘要】 针对传统隐私保护机器学习方案抵抗对抗攻击能力较弱的特点，提出一种基于格雷码置乱和分块混沌置乱的医学影像加密方案（Gray+block chaotic scrambling optimized for medical image encryption,GBCS），并应用于隐私保护的分类挖掘。首先对图像进行位平面切割；然后，对图像不同位平面进行格雷码置乱后再进行分块，在分块的基础上分别进行混沌加密；最后通过深度网络对加密后的图像进行分类学习。通过在公开乳腺癌和青光眼数据集上进行交叉验证仿真实验，对GBCS的隐私保护与分类性能进行量化分析，并从图像直方图、信息熵和对抗攻击能力等指标考虑其安全性。实验结果表明医学图像在GBCS加密前后的性能差距在可接受范围内，方案能更好地平衡性能与隐私保护的矛盾，能有效抵御对抗样本的攻击，验证了本文方法的有效性。更多还原

【Abstract】 This paper proposes a medical image encryption scheme based on Gray code scrambling and block chaotic scrambling Gray+block chaotic scrambling optimized for medical image encryption(GBCS),which is applied to privacy protection classification. First,the image is sliced by bit-planes.Then,different bit-planes of images are scrambled by the Gray code and then divided into blocks,and chaotic encryption is carried out on these blocks. Finally,the encrypted images are classified by deep learning network. We quantitatively analyze the privacy protection and classification performance of GBCS through crossvalidation simulation on public breast cancer and glaucoma datasets,and perform a safety analysis of the method by histogram,information entropy,and anti-attack ability. The experimental results prove the effectiveness of our method. The performance gap of medical images before and after GBCS encryption are within an acceptable range. The proposed scheme can better balance the contradiction between performance and privacy protection requirements,and effectively resist the attack of adversarial samples.更多还原