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Unformatted text preview: IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 1, NO. 2, JUNE 2006 275 Steganalysis for Markov Cover Data With Applications to Images Kenneth Sullivan , Member, IEEE , Upamanyu Madhow , Fellow, IEEE , Shivkumar Chandrasekaran, and B. S. Manjunath , Fellow, IEEE Abstract The difficult task of steganalysis, or the detection of the presence of hidden data, can be greatly aided by exploiting the correlations inherent in typical host or cover signals. In par- ticular, several effective image steganalysis techniques are based on the strong interpixel dependencies exhibited by natural images. Thus, existing theoretical benchmarks based on independent and identically distributed (i.i.d.) models for the cover data underesti- mate attainable steganalysis performance and, hence, overestimate the security of the steganography technique used for hiding the data. In this paper, we investigate detection-theoretic performance benchmarks for steganalysis when the cover data are modeled as a Markov chain. The main application explored here is steganalysis of data hidden in images. While the Markov chain model does not completely capture the spatial dependencies, it provides an analyt- ically tractable framework whose predictions are consistent with the performance of practical steganalysis algorithms that account for spatial dependencies. Numerical results are provided for image steganalysis of spread-spectrum and perturbed quantization data hiding. Index Terms Data hiding, Markov chain, steganalysis, steganography. I. INTRODUCTION R ESEARCH in data hiding into multimedia objects, such as music, image, and video, has advanced considerably over the past decade . Much of this work has been focused on pro- tecting the ownership rights  of digital media. In addition, the use of digital data hiding for covert communication has a long history , . As the state of the art of steganography pro- gresses, there is increased interest in steganalysis, or detection of the presence of hidden data. A review of steganalysis ,  shows many effective methods. In particular, while steganalysis is a difficult task, its performance can be greatly enhanced by exploiting the correlations inherent in typical multimedia host or cover data. For example, several effective image steganalysis techniques  are based on the strong interpixel dependen- cies exhibited by natural images. However, existing theoretical benchmarks for steganalysis are based on modeling the cover data as independent and identically distributed (i.i.d.) and, there- fore, underestimate attainable steganalysis performance. Manuscript received July 27, 2005; revised February 17, 2006. This work was supported in part by ONR Grants N0014-01-1-0380 and N0014-05-1-0816. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Jessica Fridrich....
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