Abstract-We consider the problem of detecting spatial domain least significant bit (LSB) matching steganography in grayscale images, which has proved much harder than for its counterpart, LSB replacement. We use the histogram characteristic function (HCF), introduced by Harmsen for the detection of steganography in color images but ineffective on grayscale images. Two novel ways of applying the HCF are introduced: calibrating the output using a downsampled image and computing the adjacency histogram instead of the usual histogram. Extensive experimental results show that the new detectors are reliable, vastly more so than those previously known.

Abstract. We consider methods for answering reliably the question of whether an image contains hidden data; the focus is on grayscale bitmap images and simple LSB steganography. Using a distributed computation network and a library of over 30,000 images we have been carefully evaluating the reliability of various steganalysis methods. The results suggest a number of improvements to the standard techiques, with particular benefits gained by not attempting to estimate the hidden message length. Extensive experimentation shows that the improved methods allow reliable detection of LSB steganography with between 2 and 6 times smaller embedded messages.

There has been an explosion of academic literature on steganography and steganalysis in the past two decades. With a few exceptions, such papers address abstractions of the hiding and detection problems, which arguably have become disconnected from the real world. Most published results, including by the authors of this paper, apply "in laboratory conditions" and some are heavily hedged by assumptions and caveats; significant challenges remain unsolved in order to implement good steganography and steganalysis in practice. This position paper sets out some of the important questions which have been left unanswered, as well as highlighting some that have already been addressed successfully, for steganography and steganalysis to be used in the real world.

Abstract. There are many detectors for simple Least Significant Bit (LSB) steganography in digital images, the most sensitive of which make use of structural or combinatorial properties of the LSB embedding method. We give a general framework for detection and length estimation of these hidden messages, which potentially makes use of all the combinatorial structure. The framework subsumes some previously known structural detectors and suggests novel, more powerful detection algorithms. After presenting the general framework we give a detailed study of one particular novel detector, with experimental evidence that it is more powerful than those previously known, in most cases substantially so. However there are some outstanding issues to be solved for the wider application of the general framework.

Abstract. Conventional steganalysis aims to separate cover objects from stego objects, working on each object individually. In this paper we investigate some methods for pooling steganalysis evidence, so as to obtain more reliable detection of steganography in large sets of objects, and the dual problem of hiding information securely when spreading across a batch of covers. The results are rather surprising: in many situations, a steganographer should not spread the embedding across all covers, and the secure capacity increases only as the square root of the number of objects. We validate the theoretical results, which are rather general, by testing a particular type of image steganography. The experiments involve tens of millions of repeated steganalytic attacks and show that pooled steganalysis can give very reliable detection of even tiny proportionate payloads.