用于图像认证的基于神经网络的水印技术

针对图像的真实性和完整性认证，提出一种基于神经网络的脆弱水印技术。首先，随机选取一些像素及它们的邻域；然后，用神经网络建立这些像素点与它们邻域中的其它像素点之间的关系模型，最后，通过调整被选像素点与模型输出值之间的大小关系来嵌入水印图案的位信息。根据提取的水印图案，可以判断被检测的图像是否真实以及被篡改的位置。实验结果表明，该技术在保持较高图像质量的前提下，具有安全性能好，对篡改敏感性强以及能够准确定位篡改的位置等优点。

Neural Network Based Watermarking for Image Authentication

For authenticating an image, this paper proposes a neural network-based fragile watermarking scheme. In the proposed method, we select some pixels and their vicinities randomly, and then establish the relational model among these pixels and other pixels in their vicinities using the neural network. Finally we embed a bit information of the watermark by adjusting the polarity between a selected pixel and the output value of the model. According to the extracted watermark, we can authenticate the tested image and locate the illegal modification. Experimental results show the proposed method has some advantages such as security, high sensitivity and exactly locating the modifications, while still keeping high quality of the image.