提出了一种基于深度卷积神经网络的图像哈希认证方法． 首先构建深度卷积神 经网络 AlexNet 模型，训练该网络得到预定的网络性能; 然后由训练好的卷积神经网络提 取图像的特征，最后生成图像哈希序列用于图像内容的篡改认证． 实验结果表明，相比同 类方法，文中提出的图像哈希认证方法具有较高的区分性，同时对随机攻击、 JPEG 压缩、 加性高斯噪声等具有可接受的鲁棒性． ＲOC 曲线表明，文中提出的方法实现了区分性与 鲁棒性的均衡． 

 This paper presents a scheme of deep convolution network for image hashing authentication． First，the AlexNet model of deep convolution network is constructed and the given network performance is achieved through training． Then， the trained network is used to extract image features and generate image-hashing series for content authentication． The experimental results show that in comparison with other methods，the proposed method has a higher discrimination and an acceptable robustness against content-preserving operations such as random attack， rotation，JPEG compression，and additive Gaussian noise． Receiver operating characteristics ( ROC) curve comparison demonstrates that the proposed method is able to attain a desirable compromise between the robustness and discrimination．