基于特征的静态图像内容认证方法

A Feature-based Still Image Content Authentication Algorithm

为了对受攻击的图像进行有效的认证,该文提出了一种基于特征的图像内容认证方法,该方法首先利用小波变换多尺度边缘检测方法抽取出光滑分量和边缘特征来作为生成签名的图像特征集,以保证特征集与图像之间的一一映射关系和避免伪造攻击;然后将光滑分量用32bit编码后与边缘特征以水印方式嵌入原图。验证图像时,需同时验证光滑特征和边缘特征,以确定图像内容是否被篡改。实验表明,该算法不仅可有效地检测出恶意篡改及其发生的位置,并可容忍由压缩、滤波等操作引起的失真,即使图像受到一定噪声污染时,它也可通过验证。

This paper presents a new image content authentication algorithm based on image feature for effectively authenticate the tampered image. First the smooth-component and edge-character are drawn from the image with the dyadic wavelet transform multi-scale edge detection, which form an image feature set for generating the digital signatures. This can ensure the one-to-one mapping relationship between the feature set and the image, and avoid the counterfeit attack. After 32 bits coding, the smooth-component is embedded in the original image with the edge-character as watermark. When the image is verified, the smooth character and edge character are compared to confirm whether the content has been tampered. The experiments prove that this authentication algorithm can effectively detect the event and the location of vicious tamper, and it can also tolerate the damage produced by compression, filter and some other image procession. Even if the image is degraded by some noise, it can still pass through the authentication correctly.