Effect of Acquisition Noise Outliers on Steganalysis

Understanding the mechanisms that lead to false alarms (erroneously detecting cover images as containing secrets) in steganalysis is a topic of utmost importance for practical applications.In this paper, we present evidence that a relatively small number of pixel outliers introduced by the image acquisition process can skew the soft output of a data driven detector to produce a strong false alarm.To verify this hypothesis, for a cover image we estimate a statistical model of the acquisition noise in the developed domain and identify pixels that contribute the most to the associated likelihood ratio test (LRT) for steganography.We call such cover elements LIEs (Locally Influential Elements).The effect of LIEs on the output of a data-driven detector is demonstrated by turning a strong false alarm into a correctly classified cover by introducing a relatively small number of "de-embedding" changes at LIEs.Similarly, we show that it is possible to introduce a small number of LIEs into a strong cover to make a data driven detector classify it as stego.Our findings are supported by experiments on two datasets with three steganographic algorithms and four types of data driven detectors.