With upcoming heavy and superheavy lift rockets targeted for launch in 2021, a new class of payloads can be launched into low Earth orbit. This paper presents a design for a space station capable of being lifted by a single Blue Origin New Glenn vehicle. Once assembled on orbit, it contains more than twice the pressurized volume of the International Space Station (ISS). The pressurized volume of traditional space station modules is limited by the dimensions of the payload fairing. The proposed Orb2 design consists of a spherical habitation module and a service module. The habitation module avoids the volume limitation by being launched in a flat-packed stack and assembled and welded robotically in orbit. Using CAD and finite element analysis, this paper shows that the habitation module contains approximately 2000m3 of pressurized volume, with a mass of 24 tons, and can hold four times the standard atmospheric pressure. Moreover, the Whipple shield configuration is identical to the heavily protected sections of the ISS. The service module has a traditional cylindrical design, and its role is to provide all the necessary utilities. Furthermore, the paper explains the assembly and welding method required to convert Orb2 into the fully assembled space station.

State-of-the-art JPEG steganographic algorithms, such as J-UNIWARD, are currently better detected in the spatial domain rather than the JPEG domain. Rich models built from pixel residuals seem to better capture the impact of embedding than features constructed as co-occurrences of quantized JPEG coefficients. However, when steganalyzing JPEG steganographic algorithms in the spatial domain, the pixels’ statistical properties vary because of the underlying 8 × 8 pixel grid imposed by the compression. In order to detect JPEG steganography more accurately, we split the statistics of noise residuals based on their phase w.r.t. the 8 × 8 grid. Because of the heterogeneity of pixels in a decompressed image, it also makes sense to keep the kernel size of pixel predictors small as larger kernels mix up qualitatively different statistics more, losing thus on the detection power. Based on these observations, we propose a novel feature set called PHase Aware pRojection Model (PHARM) in which residuals obtained using a small number of small-support kernels are represented using first-order statistics of their random projections as in the projection spatial rich model PSRM. The benefit of making the features “phase-aware” is shown experimentally on selected modern JPEG steganographic algorithms with the biggest improvement seen for J-UNIWARD. Additionally, the PHARM feature vector can be computed at a fraction of computational costs of existing projection rich models.

The design of both steganography and steganalysis methods for digital images heavily relies on empirically justified principles. In steganography, the domain in which the embedding changes are executed is usually the preferred domain in which to measure the statistical impact of embedding (to construct the distortion function). Another principle almost exclusively used in steganalysis states that the most accurate detection is obtained when extracting the steganalysis features from the embedding domain. While a substantial body of prior art seems to support these two doctrines, this article challenges both principles when applied to the JPEG format. Through a series of targeted experiments on numerous older as well as current steganographic algorithms, we lay out arguments for why measuring the embedding distortion in the spatial domain can be highly beneficial for JPEG steganography. Moreover, as modern embedding algorithms avoid introducing easily detectable artifacts in the statistics of quantized DCT coefficients, we demonstrate that more accurate detection is obtained when constructing the steganalysis features in the spatial domain where the distortion function is minimized, challenging thus both established doctrines.

Recently, a new steganographic method was introduced that utilizes a universal distortion function called UNIWARD. The distortion between the cover and stego image is computed as a sum of relative changes of wavelet coefficients representing both images. As already pointed out in the original publication, the selection channel of the spatial version of UNIWARD (the version that hides messages in pixel values called S-UNIWARD) exhibits unusual properties – in highly textured and noisy regions the embedding probabilities form interleaved streaks of low and high embedding probability. While the authors of UNIWARD themselves hypothesized that such an artifact in the embedding probabilities may jeopardize its security, experiments with state-of-the-art rich models did not reveal any weaknesses. Using the fact that the cover embedding probabilities can be approximately estimated from the stego image, we introduce the novel concept of content-selective residuals and successfully attack S-UNIWARD. We also show that this attack, which is made possible by a faulty probabilistic selection channel, can be prevented by properly adjusting the stabilizing constant in the UNIWARD distortion function.

The "Internet of Things" is an appealing concept aiming to assign digital identity to both physical and digital everyday objects. One way of achieving this goal is to embed the identity in the object itself by using digital watermarking. In the case of printed physical objects, such as consumer packages, this identity can be later read from a digital image of the watermarked object taken by a camera. In many cases, the object might occupy only a small portion of the the image and an attempt to read the watermark payload from the whole image can lead to unnecessary processing. This paper proposes a statistical learning-based algorithm for localizing watermarked physical objects taken by a digital camera. The algorithm is specifically designed and tested on watermarked consumer packages read by an off-the-shelf barcode imaging scanner. By employing simple noise-sensitive features borrowed from blind image steganalysis and a linear classifier, we are able to estimate probabilities of watermark presence in every part of the image significantly faster than running a watermark detector. These probabilities are used to pinpoint areas that are recommended for further processing. We compare our adaptive approach with a system designed to read watermarks from a set of fixed locations and achieve significant savings in processing time while improving overall detector robustness.

This paper introduces a novel feature set for steganalysis of JPEG images. The features are engineered as first-order statistics of quantized noise residuals obtained from the decompressed JPEG image using 64 kernels of the discrete cosine transform (DCT) (the so-called undecimated DCT). This approach can be interpreted as a projection model in the JPEG domain, forming thus a counterpart to the projection spatial rich model. The most appealing aspect of this proposed steganalysis feature set is its low computational complexity, lower dimensionality in comparison with other rich models, and a competitive performance with respect to previously proposed JPEG domain steganalysis features.

Today, the most reliable detectors of steganography in empirical cover sources, such as digital images coming from a known source, are built using machine-learning by representing images with joint distributions (co-occurrences) of neighboring noise residual samples computed using local pixel predictors. In this paper, we propose an alternative statistical description of residuals by binning their random projections on local neighborhoods. The size and shape of the neighborhoods allow the steganalyst to further diversify the statistical description and thus improve detection accuracy, especially for highly adaptive steganography. Other key advantages of this approach include the possibility to model long-range dependencies among pixels and making use of information that was previously underutilized in the marginals of co-occurrences. Moreover, the proposed approach is much more flexible than the previously proposed spatial rich model, allowing the steganalyst to obtain a significantly better trade off between detection accuracy and feature dimensionality. We call the new image representation the Projection Spatial Rich Model (PSRM) and demonstrate its effectiveness on HUGO and WOW – two current state-of-the-art spatial-domain embedding schemes.

Currently, the most secure practical steganographic schemes for empirical cover sources embed their payload while minimizing a distortion function designed to capture statistical detectability. Since there exists a general framework for this embedding paradigm with established payload-distortion bounds as well as near-optimal practical coding schemes, building an embedding scheme has been essentially reduced to the distortion design. This is not an easy task as relating distortion to statistical detectability is a hard and open problem. In this article, we propose an innovative idea to measure the embedding distortion in one fixed domain independently of the domain where the embedding changes (and coding) are carried out. The proposed universal distortion is additive and evaluates the cost of changing an image element (e.g., pixel or DCT coefficient) from directional residuals obtained using a Daubechies wavelet filter bank. The intuition is to limit the embedding changes only to those parts of the cover that are difficult to model in multiple directions while avoiding smooth regions and clean edges. The utility of the universal distortion is demonstrated by constructing steganographic schemes in the spatial, JPEG, and side-informed JPEG domains, and comparing their security to current state-of-the-art methods using classifiers trained with rich media models.

The traditional way to represent digital images for feature based steganalysis is to compute a noise residual from the image using a pixel predictor and then form the feature as a sample joint probability distribution of neighboring quantized residual samples—the so-called co-occurrence matrix. In this paper, we propose an alternative statistical representation—instead of forming the co-occurrence matrix, we project neighboring residual samples onto a set of random vectors and take the first-order statistic (histogram) of the projections as the feature. When multiple residuals are used, this representation is called the projection spatial rich model (PSRM). On selected modern steganographic algorithms embedding in the spatial, JPEG, and side-informed JPEG domains, we demonstrate that the PSRM can achieve a more accurate detection as well as a substantially improved performance versus dimensionality trade-off than state-of-the-art feature sets.

A standard way to design steganalysis features for digital images is to choose a pixel predictor, use it to compute a noise residual, and then form joint statistics of neighboring residual samples (co-occurrence matrices). This paper proposes a general data-driven approach to optimizing predictors for steganalysis. First, a local pixel predictor is parametrized and then its parameters are determined by solving an optimization problem for a given sample of cover and stego images and a given cover source. Our research shows that predictors optimized to detect a specific case of steganography may be vastly different than predictors optimized for the cover source only. The results indicate that optimized predictors may improve steganalysis by a rather non-negligible margin. Furthermore, we construct the predictors sequentially - having optimized k predictors, design the k + 1st one with respect to the combined feature set built from all k predictors. In other words, given a feature space (image model) extend (diversify) the model in a selected direction (functional form of the predictor) in a way that maximally boosts detection accuracy.

This paper presents a new approach to defining additive steganographic distortion in the spatial domain. The change in the output of directional high-pass filters after changing one pixel is weighted and then aggregated using the reciprocal Hölder norm to define the individual pixel costs. In contrast to other adaptive embedding schemes, the aggregation rule is designed to force the embedding changes to highly textured or noisy regions and to avoid clean edges. Consequently, the new embedding scheme appears markedly more resistant to steganalysis using rich models. The actual embedding algorithm is realized using syndrome-trellis codes to minimize the expected distortion for a given payload.

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.

This paper describes our experience with the BOSS competition in chronological order. The intention is to reveal all details of our effort focused on breaking HUGO – one of the most advanced steganographic systems ever published. We believe that researchers working in steganalysis of digital media and related fields will find it interesting, inspiring, and perhaps even entertaining to read about the details of our journey, including the dead ends, false hopes, surprises, obstacles, and lessons learned. This information is usually not found in technical papers that only show the final polished approach. This work accompanies our other paper in this volume [9].

A modern direction in steganography calls for embedding while minimizing a distortion function defined in a sufficiently complex model space. In this paper we show that, quite surprisingly, even a high-dimensional cover model does not automatically guarantee immunity to simple attacks. Moreover, the security can be compromised if the distortion is optimized to an incomplete cover model. We demonstrate these pitfalls with two recently proposed steganographic schemes and support our arguments experimentally. Finally, we discuss how the corresponding models might be modified to eliminate the security flaws.

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.