Robust Image Hashing Using Higher Order Spectral Features

Brenden Chen, V. Chandran
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引用次数: 5

Abstract

Robust image hashing seeks to transform a given input image into a shorter hashed version using a key-dependent non-invertible transform. These image hashes can be used for watermarking, image integrity authentication or image indexing for fast retrieval. This paper introduces a new method of generating image hashes based on extracting Higher Order Spectral features from the Radon projection of an input image. The feature extraction process is non-invertible, non-linear and different hashes can be produced from the same image through the use of random permutations of the input. We show that the transform is robust to typical image transformations such as JPEG compression, noise, scaling, rotation, smoothing and cropping. We evaluate our system using a verification-style framework based on calculating false match, false non-match likelihoods using the publicly available Uncompressed Colour Image database (UCID) of 1320 images. We also compare our results to Swaminathan’s Fourier-Mellin based hashing method with at least 1% EER improvement under noise, scaling and sharpening.
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基于高阶谱特征的鲁棒图像哈希
鲁棒图像哈希寻求使用依赖键的不可逆变换将给定的输入图像转换为更短的哈希版本。这些图像哈希可以用于水印,图像完整性认证或快速检索图像索引。介绍了一种基于从输入图像的Radon投影中提取高阶谱特征来生成图像哈希的新方法。特征提取过程是不可逆的,非线性的,通过使用输入的随机排列可以从同一图像产生不同的哈希值。我们表明,该变换对典型的图像变换(如JPEG压缩、噪声、缩放、旋转、平滑和裁剪)具有鲁棒性。我们使用基于计算假匹配的验证式框架来评估我们的系统,假不匹配的可能性使用1320张图像的公开可用的未压缩彩色图像数据库(UCID)。我们还将我们的结果与Swaminathan的基于Fourier-Mellin的哈希方法进行了比较,在噪声,缩放和锐化下,我们的EER至少提高了1%。
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