Review: Digital image steganography: Survey and analysis of current methods

We describe a novel general strategy for building steganography detectors for digital images. The process starts with assembling a rich model of the noise component as a union of many diverse submodels formed by joint distributions of neighboring samples from quantized image noise residuals obtained using linear and nonlinear high-pass filters. In contrast to previous approaches, we make the model assembly a part of the training process driven by samples drawn from the corresponding cover- and stego-sources. Ensemble classifiers are used to assemble the model as well as the final steganalyzer due to their low computational complexity and ability to efficiently work with high-dimensional feature spaces and large training sets. We demonstrate the proposed framework on three steganographic algorithms designed to hide messages in images represented in the spatial domain: HUGO, edge-adaptive algorithm by Luo , and optimally coded ternary ±1 embedding. For each algorithm, we apply a simple submodel-selection technique to increase the detection accuracy per model dimensionality and show how the detection saturates with increasing complexity of the rich model. By observing the differences between how different submodels engage in detection, an interesting interplay between the embedding and detection is revealed. Steganalysis built around rich image models combined with ensemble classifiers is a promising direction towards automatizing steganalysis for a wide spectrum of steganographic schemes.

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Rich Models for Steganalysis of Digital Images

Today, the most accurate steganalysis methods for digital media are built as supervised classifiers on feature vectors extracted from the media. The tool of choice for the machine learning seems to be the support vector machine (SVM). In this paper, we propose an alternative and well-known machine learning tool-ensemble classifiers implemented as random forests-and argue that they are ideally suited for steganalysis. Ensemble classifiers scale much more favorably w.r.t. the number of training examples and the feature dimensionality with performance comparable to the much more complex SVMs. The significantly lower training complexity opens up the possibility for the steganalyst to work with rich (high-dimensional) cover models and train on larger training sets-two key elements that appear necessary to reliably detect modern steganographic algorithms. Ensemble classification is portrayed here as a powerful developer tool that allows fast construction of steganography detectors with markedly improved detection accuracy across a wide range of embedding methods. The power of the proposed framework is demonstrated on three steganographic methods that hide messages in JPEG images.

/pdf/ensemble-classifiers-for-steganalysis-of-digital-media-14urqbpk2p.pdf

Ensemble Classifiers for Steganalysis of Digital Media

This paper presents a method for detection of steganographic methods that embed in the spatial domain by adding a low-amplitude independent stego signal, an example of which is least significant bit (LSB) matching. First, arguments are provided for modeling the differences between adjacent pixels using first-order and second-order Markov chains. Subsets of sample transition probability matrices are then used as features for a steganalyzer implemented by support vector machines. The major part of experiments, performed on four diverse image databases, focuses on evaluation of detection of LSB matching. The comparison to prior art reveals that the presented feature set offers superior accuracy in detecting LSB matching. Even though the feature set was developed specifically for spatial domain steganalysis, by constructing steganalyzers for ten algorithms for JPEG images, it is demonstrated that the features detect steganography in the transform domain as well.

/pdf/steganalysis-by-subtractive-pixel-adjacency-matrix-4ov6mwe1p3.pdf

Steganalysis by Subtractive Pixel Adjacency Matrix

This paper summarizes the first international challenge on steganalysis called BOSS (an acronym for Break Our Steganographic System). We explain the motivations behind the organization of the contest, its rules together with reasons for them, and the steganographic algorithm developed for the contest. Since the image databases created for the contest significantly influenced the development of the contest, they are described in a great detail. Paper also presents detailed analysis of results submitted to the challenge. One of the main difficulty the participants had to deal with was the discrepancy between training and testing source of images - the so-called cover-source mismatch, which forced the participants to design steganalyzers robust w.r.t. a specific source of images. We also point to other practical issues related to designing steganographic systems and give several suggestions for future contests in steganalysis.

/pdf/break-our-steganographic-system-the-ins-and-outs-of-2idx3tx1us.pdf

Break our steganographic system: the ins and outs of organizing BOSS

The goal of this paper is to determine the steganographic capacity of JPEG images (the largest payload that can be undetectably embedded) with respect to current best steganalytic methods. Additionally, by testing selected steganographic algorithms we evaluate the influence of specific design elements and principles, such as the choice of the JPEG compressor, matrix embedding, adaptive content-dependent selection channels, and minimal distortion steganography using side information at the sender. From our experiments, we conclude that the average steganographic capacity of grayscale JPEG images with quality factor 70 is approximately 0.05 bits per non-zero AC DCT coefficient.

/pdf/statistically-undetectable-jpeg-steganography-dead-ends-3f3wfuzhqs.pdf

Statistically undetectable jpeg steganography: dead ends challenges, and opportunities

In this paper, we propose a rich model of DCT coefficients in a JPEG file for the purpose of detecting steganographic
embedding changes. The model is built systematically as a union of smaller submodels formed as joint
distributions of DCT coefficients from their frequency and spatial neighborhoods covering a wide range of statistical
dependencies. Due to its high dimensionality, we combine the rich model with ensemble classifiers and
construct detectors for six modern JPEG domain steganographic schemes: nsF5, model-based steganography,
YASS, and schemes that use side information at the embedder in the form of the uncompressed image: MME,
BCH, and BCHopt. The resulting performance is contrasted with previously proposed feature sets of both low
and high dimensionality. We also investigate the performance of individual submodels when grouped by their
type as well as the effect of Cartesian calibration. The proposed rich model delivers superior performance across
all tested algorithms and payloads.

/pdf/steganalysis-of-jpeg-images-using-rich-models-5c7gip3l3x.pdf

Steganalysis of JPEG Images Using Rich Models

Calibration was first introduced in 2002 as a new concept to attack the F5 algorithm [3]. Since then, it became an essential part of many feature-based blind and targeted steganalyzers in JPEG as well as spatial domain. The purpose of this paper is to shed more light on how, why, and when calibration works. In particular, this paper challenges the thesis that the purpose of calibration is to estimate cover image features from the stego image. We classify calibration according to its internal mechanism into several canonical examples, including the case when calibration hurts the detection performance. All examples are demonstrated on specific steganographic schemes and steganalysis features. Furthermore, we propose a modified calibration procedure that improves practical steganalysis.

Calibration revisited

By working with high-dimensional representations of covers, modern steganographic methods are capable of
preserving a large number of complex dependencies among individual cover elements and thus avoid detection
using current best steganalyzers. Inevitably, steganalysis needs to start using high-dimensional feature sets as
well. This brings two key problems - construction of good high-dimensional features and machine learning that
scales well with respect to dimensionality. Depending on the classifier, high dimensionality may lead to problems
with the lack of training data, infeasibly high complexity of training, degradation of generalization abilities, lack
of robustness to cover source, and saturation of performance below its potential. To address these problems
collectively known as the curse of dimensionality, we propose ensemble classifiers as an alternative to the much
more complex support vector machines. Based on the character of the media being analyzed, the steganalyst first
puts together a high-dimensional set of diverse "prefeatures" selected to capture dependencies among individual
cover elements. Then, a family of weak classifiers is built on random subspaces of the prefeature space. The
final classifier is constructed by fusing the decisions of individual classifiers. The advantage of this approach is
its universality, low complexity, simplicity, and improved performance when compared to classifiers trained on
the entire prefeature set. Experiments with the steganographic algorithms nsF5 and HUGO demonstrate the
usefulness of this approach over current state of the art.

/pdf/steganalysis-in-high-dimensions-fusing-classifiers-built-on-1endc0q3m7.pdf

Steganalysis in high dimensions: fusing classifiers built on random subspaces

Content-adaptive steganography constrains its embedding changes to those parts of covers that are difficult to model, such as textured or noisy regions. When combined with advanced coding techniques, adaptive steganographic methods can embed rather large payloads with low statistical detectability at least when measured using feature-based steganalyzers trained on a given cover source. The recently proposed steganographic algorithm HUGO is an example of this approach. The goal of this paper is to subject this newly proposed algorithm to analysis, identify features capable of detecting payload embedded using such schemes and obtain a better picture regarding the benefit of adaptive steganography with public selection channels. This work describes the technical details of our attack on HUGO as part of the BOSS challenge.

/pdf/steganalysis-of-content-adaptive-steganography-in-spatial-1ccp6g3u4f.pdf

Jan Kodovský

Papers

Statistically undetectable jpeg steganography: dead ends challenges, and opportunities

Steganalysis of JPEG Images Using Rich Models

Calibration revisited

Steganalysis in high dimensions: fusing classifiers built on random subspaces

Steganalysis of content-adaptive steganography in spatial domain