Avcibaş Ismail, Memon Nasir, Sankur Bülent
Dept. of Electron. Eng., Uludag Univ., Bursa, Turkey.
IEEE Trans Image Process. 2003;12(2):221-9. doi: 10.1109/TIP.2002.807363.
We present techniques for steganalysis of images that have been potentially subjected to steganographic algorithms, both within the passive warden and active warden frameworks. Our hypothesis is that steganographic schemes leave statistical evidence that can be exploited for detection with the aid of image quality features and multivariate regression analysis. To this effect image quality metrics have been identified based on the analysis of variance (ANOVA) technique as feature sets to distinguish between cover-images and stego-images. The classifier between cover and stego-images is built using multivariate regression on the selected quality metrics and is trained based on an estimate of the original image. Simulation results with the chosen feature set and well-known watermarking and steganographic techniques indicate that our approach is able with reasonable accuracy to distinguish between cover and stego images.
我们展示了用于对可能已遭受隐写算法处理的图像进行隐写分析的技术，这些技术适用于被动监控和主动监控框架。我们的假设是，隐写方案会留下统计证据，借助图像质量特征和多元回归分析可用于检测。为此，基于方差分析（ANOVA）技术确定了图像质量指标作为特征集，以区分载体图像和隐写图像。使用所选质量指标通过多元回归构建载体图像和隐写图像之间的分类器，并基于原始图像的估计进行训练。使用所选特征集以及知名水印和隐写技术的仿真结果表明，我们的方法能够以合理的准确率区分载体图像和隐写图像。
