Title :
Optimal Cover Estimation Methods and Steganographic Payload Location
Author_Institution :
Sandia Nat. Labs., Albuquerque, NM, USA
Abstract :
Cover estimation is an important part of steganalysis and has many applications. One such application is steganographic payload location using residuals, which is effective when a large number of stego images are available. In the ideal case when the cover images are available, we show that the expected number of stego images needed to perfectly locate all load-carrying pixels is approximately the logarithm of the payload size. In more practical settings when the cover images are not available, the accuracy of payload location depends primarily on the chosen cover estimation method. We present optimal, linear runtime algorithms for finding the most likely cover estimate given the stego image and experimentally demonstrate that they can be used to locate payload on both least-significant bit (LSB) replacement and LSB matching stego images. The algorithms can be extended to higher order statistical models of cover images.
Keywords :
image coding; image matching; steganography; cover estimation method; cover image; least-significant bit replacement; runtime algorithm; steganalysis; steganographic payload location; steganographic residual; stego image matching; Computational modeling; Hidden Markov models; Markov processes; Payloads; Steganography; Viterbi algorithm; Cover estimation; Viterbi decoding; payload location; steganalysis;
Journal_Title :
Information Forensics and Security, IEEE Transactions on
DOI :
10.1109/TIFS.2011.2160855