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A Novel Approach for Robust Perceptual Image Hashing

机译:一种鲁棒性感知图像散列的新方法

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Perceptual image hashing system generates a short signature called perceptual hash attached to an image before transmission and acts as side information for analyzing the trustworthiness of the received image. In this paper, we propose a novel approach to improve robustness for perceptual image hashing scheme for generating a perceptual hash that should be resistant to content-preserving manipulations, such as JPEG compression and Additive white Gaussian noise (AWGN) also should differentiate the maliciously tampered image and its original version. Our algorithm first constructs a robust image, derived from the original input by analyzing the stability of the extracted features and improving their robustness. From the robust image, which does perceptually resemble the original input, we further extract the final robust features. Next, robust features are suitably quantized allowing the generation of the final perceptual hash using the cryptographic hash function SHA1. The main idea of this paper is to transform the original image into a more robust one that allows the extraction of robust features. Generation of the robust image turns out be quite important since it introduces further robustness to the perceptual image hashing system. The paper can be seen as an attempt to propose a general methodology for more robust perceptual image hashing. The experimental results presented in this paper reveal that the proposed scheme offers good robustness against JPEG compression and Additive white Gaussian noise.
机译:感知图像散列系统生成称为感知散列的短签名,其在传输之前附加到图像,并充当用于分析所接收图像的可信度的侧面信息。在本文中,我们提出了一种新的方法来改善感知图像散列方案的鲁棒性,用于生成感知哈希值,应该抵抗内容保存的操纵,例如JPEG压缩和添加剂白色高斯噪声(AWGN)也应该区分恶意篡改图像及其原始版本。我们的算法首先通过分析提取的特征的稳定性并提高其稳健性来构造从原始输入的稳健图像构造。从强大的图像中,这是感知上的最初输入,我们进一步提取了最终的鲁棒特征。接下来,适当地量化鲁棒特征,允许使用加密散列函数SHA1生成最终感知哈希。本文的主要思想是将原始图像转换为更强大的图像,允许提取鲁棒特征。鲁棒图像的产生结果非常重要,因为它引入了对感知图像散列系统的进一步稳健性。本文可以被视为试图提出一种用于更加稳健的感知图像散列的一般方法。本文提出的实验结果表明,该方案对JPEG压缩和添加剂白色高斯噪声提供了良好的鲁棒性。

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