Abstract
Digital steganographic algorithms hide secret messages in seemingly innocent cover objects, such as images. Steganographic algorithms are rapidly evolving, reducing distortions, and making detection of altered cover objects by steganalysis algorithms more challenging. The value of current steganographic and steganalysis algorithms is difficult to evaluate until they are tested on realistic datasets. We propose a system approach to steganalysis for reliably detecting steganographic objects among a large number of images, acknowledging that most digital images are intact. The system consists of a cascade of intrinsic image formations filters (IIFFs), where the IIFFs in the early stage are designed to filter out non-stego images based on real world constraints, and the IIFFs in the late stage are designed to detect intrinsic features of specific steganographic routines. Our approach makes full use of all available constraints, leading to robust detection performance and low probability of false alarm. Our results based on a large image set from Flickr.com demonstrate the potential of our approach on large-scale real-world repositories.
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