一种基于遗传算法和BP网络的鲁棒图像哈希方法

提出一种用于图像内容认证的基于遗传算法和BP网络（GA-BP）的鲁棒图像哈希方法。运用提升小波变换（lifting wavelet transform，LWT）得到图像的低频分量，对低频分量进行离散傅里叶变换（discrete Fourier transform，DFT）提取幅度和相位信息以建立图像的特征矩阵，利用构建的GA-BP模型，生成鲁棒的图像哈希序列并用于图像内容的篡改认证。实验结果表明，相比于同类方法，所提出的图像哈希认证方法对随机攻击、旋转、JPEG压缩，加性高斯噪声等具有较好的鲁棒性和区分性。

Robust Image Hashing Based on Genetic Algorithm and BP Network

This paper presents a robust image-hashing scheme based on genetic algorithm (GA) and back propagation (BP) neural network for content authentication. Lifting wavelet transform and discrete Fourier transform are used to extract the image's amplitude spectrum and phase spectrum information to create an image feature matrix. A GA-BP network model is established, and used to generate an image Hash sequence for content authentication. Experimental results show that the proposed Hashing method is robust against content-preserving operations such as random attack, rotation, JPEG compression and additive Gaussian noise. The proposed approach is significantly superior to other algorithms in terms of robustness and discrimination.