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首页> 外文期刊>International journal of digital crime and forensics >Localization of Tampering Created with Facebook Images by Analyzing Block Factor Histogram Voting
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Localization of Tampering Created with Facebook Images by Analyzing Block Factor Histogram Voting

机译：通过分析块因子直方图投票对Facebook图像创建的篡改进行本地化

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摘要

Facebook images get distributed within a fraction of a second, which hackers may tamper and redistribute on cyberspace. JPEG fingerprint based tampering detection techniques have major scope in tampering localization within standard JPEG images. The majority of these algorithms fails to detect tampering created using Facebook images. Facebook utilizes down-sampling followed by compression, which makes difficult to locate tampering created with these images. In this paper, the authors have proposed the tampering localization algorithm, which locates tampering created with the images downloaded from Facebook The algorithm uses Factor Histogram of DCT coefficients at first 15 modes to find primary quantization steps. The image is divided into BXB overlapping blocks and each block is processed individually. Votes cast by these modes for conceivable tampering are collected at every pixel position and the ones above threshold are used to form different regions. High density voted region is proclaimed as tampered region.
机译：Facebook图片在不到一秒钟的时间内就可以分发，黑客可能会篡改并在网络空间上进行重新分发。基于JPEG指纹的篡改检测技术在标准JPEG图像内的篡改本地化方面具有主要范围。这些算法大多数无法检测使用Facebook图像创建的篡改。 Facebook利用降采样后再进行压缩，这使得难以定位使用这些图像创建的篡改。在本文中，作者提出了一种篡改定位算法，该算法可定位从Facebook下载的图像创建的篡改。该算法在前15个模式中使用DCT系数的因子直方图来查找主要的量化步骤。图像被分为BXB重叠块，每个块分别进行处理。这些模式投下的可想而知的篡改票数会在每个像素位置收集，高于阈值的票数将用于形成不同的区域。高密度投票区域被宣布为篡改区域。

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