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Improved stego sensitivity measure for ±A steganalysis

机译:改进的sego敏感度量±steganysis

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This article addresses four basic goals; 1) the evaluation of sequentially and randomly embedded stego evidence within digital images 2) the identification of "steganographic fingerprint" for special domain based steganographic methods, 3) the reduction of steganalysis false detection rate, and 4) the investigation of two well known pixel comparison based steganalysis methods. We present an improved version of Stego Sensitivity Measure, which is based on the statistics of sample pair (the basic unit), rather than individual samples which is very sensitive to ±A embedding. The presented measure enhances stego detection accuracy and localization of stego areas within sequentially and randomly embedded color or gray scale stego images. In addition, it estimates the message length of an embedded bit-stream within bit planes of a digital image, and it has better localization of steganography detected along with an improved estimation of the message length. It also identifies the "steganographic fingerprint" of special domain sequentially and randomly based steganographic methods. Numerical experimentation was conducted with an arbitrary image database of 200 color TIFF and RAW images taken with the Nikon D100 and the Canon EOS Digital Rebel cameras. In this article comparison are also shown using two known steganalysis methods Raw Quick Pairs and RS Steganalysis which have revealed that; a) The false alarm rate for the proposed detection method is p = 0.9 for a database of 200 images clean images while RS Steganalysis has shown a high false alarm rate for clean images of p = 2.8. b) The two methods Raw Quick Pairs and RS Steganalysis cannot be used for localization of steganographic regions due to the statistical properties of the detection methods.
机译:本文涉及四个基本目标; 1)评估数字图像中的顺序和随机嵌入的STEGO证据2)鉴定基于特殊结构域的书签方法的“隐写指纹”,3)减少麻痹假检出率,4)对两个公知的像素的研究基于比较的塞析分析方法。我们提出了一种改进的STEGO灵敏度测量版本,其基于样本对(基本单元)的统计,而不是对嵌入±嵌入非常敏感的单独样本。所提出的措施增强了顺序和随机嵌入的颜色或灰度标记图像内的STEGO检测精度和STEGO区域的定位。另外,估计数字图像的位平面内的嵌入比特流的消息长度,并且它具有更好地检测到的隐写术的定位,以及对消息长度的改进估计。它还依次识别特殊域的“隐写指纹”,并基于随机的隐写方法。用尼康D100和佳能EOS数字反叛摄像机拍摄的200个颜色TIFF和RAW图像的任意图像数据库进行数值实验。在本文中,还使用两种已知的塞巴巴分析方法进行了比较,原始快速对和RS隐藏透露了这一点; a)建议的检测方法的误报率为P = 0.9,对于200张图像的数据库清洁图像,而RS麻痹已经显示出P = 2.8的清洁图像的高误报率。 b)由于检测方法的统计特性,这两种方法原始快速对和Rs托巴分析不能用于定位书签区域。

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