skip to main content


Title: Revisiting Perturbed Quantization
In this work, we revisit Perturbed Quantization steganography with modern tools available to the steganographer today, including near-optimal ternary coding and content-adaptive embedding with side-information. In PQ, side-information in the form of rounding errors is manufactured by recompressing a JPEG image with a ju- diciously selected quality factor. This side-information, however, cannotbeusedinthesamefashionasinconventionalside-informed schemes nowadays as this leads to highly detectable embedding. As a remedy, we utilize the steganographic Fisher information to allocate the payload among DCT modes. In particular, we show that the embedding should not be constrained to contributing coef- ficients only as in the original PQ but should be expanded to the so-called “contributing DCT modes.” This approach is extended to color images by slightly modifying the SI-UNIWARD algorithm. Using the best detectors currently available, it is shown that by manufacturing side information with double compression, one can embedthesameamountofinformationintothedoubly-compressed cover image with a significantly better security than applying J- UNIWARD directly in the single-compressed image. At the end of the paper, we show that double compression with the same qual- ity makes side-informed steganography extremely detectable and should be avoided.  more » « less
Award ID(s):
2028119
NSF-PAR ID:
10301790
Author(s) / Creator(s):
;
Date Published:
Journal Name:
ACM Information Hiding and Multimedia Security Workshop
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. null (Ed.)
    The steganographic field is nowadays dominated by heuristic approaches for data hiding. While there exist a few model-based steganographic algorithms designed to minimize statistical detectability of the underlying model, many more algorithms based on costs of changing a specific pixel or a DCT coefficient have been over the last decade introduced. These costs are purely heuristic, as they are designed with feedback from detectors implemented as machine learning classifiers. For this reason, there is no apparent relation to statistical detectability, even though in practice they provide comparable security to model-based algorithms. Clearly, the security of such algorithms stands only on the assumption, that the detector used to assess the security, is the best one possible. Such assumption is of course completely unrealistic. Similarly, steganalysis is mainly implemented with empirical machine learning detectors, which use hand-crafted features computed from images or as deep learning detectors - convolutional neural networks. The biggest drawback of this approach is, that the steganalyst, even though having a very good detection power, has very little to no knowledge about what part of the image or the embedding algorithm contributes to the detection, because the detector is used as a black box. In this work, we will try to leave the heuristics behind and go towards statistical models. First, we introduce statistical models for current heuristic algorithms, which helps us understand and predict their security trends. Furthemore this allows us to improve the security of such algorithms. Next, we focus on steganalysis exploiting universal properties of JPEG images. Under certain realistic conditions, this leads to a very powerful attack against any steganography, because embedding even a very small secret message breaks the statistical model. Lastly, we show how we can improve security of JPEG compressed images through additional compression. 
    more » « less
  2. null (Ed.)
    While convolutional neural networks have firmly established themselves as the superior steganography detectors, little human-interpretable feedback to the steganographer as to how the network reaches its decision has so far been obtained from trained models. The folklore has it that, unlike rich models, which rely on global statistics, CNNs can leverage spatially localized signals. In this paper, we adapt existing attribution tools, such as Integrated Gradients and Last Activation Maps, to show that CNNs can indeed find overwhelming evidence for steganography from a few highly localized embedding artifacts. We look at the nature of these artifacts via case studies of both modern content-adaptive and older steganographic algorithms. The main culprit is linked to “content creating changes” when the magnitude of a DCT coefficient is increased (Jsteg, –F5), which can be especially detectable for high frequency DCT modes that were originally zeros (J-MiPOD). In contrast, J- UNIWARD introduces the smallest number of locally detectable embedding artifacts among all tested algorithms. Moreover, we find examples of inhibition that facilitate distinguishing between the selection channels of stego algorithms in a multi-class detector. The authors believe that identifying and characterizing local embedding artifacts provides useful feedback for future design of steganographic schemes. 
    more » « less
  3. In this article, we study a recently proposed method for improving empirical security of steganography in JPEG images in which the sender starts with an additive embedding scheme with symmetrical costs of ±1 changes and then decreases the cost of one of these changes based on an image obtained by applying a deblocking (JPEG dequantization) algorithm to the cover JPEG. This approach provides rather significant gains in security at negligible embedding complexity overhead for a wide range of quality factors and across various embedding schemes. Challenging the original explanation of the inventors of this idea, which is based on interpreting the dequantized image as an estimate of the precover (uncompressed) image, we provide alternative arguments. The key observation and the main reason why this approach works is how the polarizations of individual DCT coefficients work together. By using a MiPOD model of content complexity of the uncompressed cover image, we show that the cost polarization technique decreases the chances of “bad” combinations of embedding changes that would likely be introduced by the original scheme with symmetric costs. This statement is quantified by computing the likelihood of the stego image w.r.t. the multivariate Gaussian precover distribution in DCT domain. Furthermore, it is shown that the cost polarization decreases spatial discontinuities between blocks (blockiness) in the stego image and enforces desirable correlations of embedding changes across blocks. To further prove the point, it is shown that in a source that adheres to the precover model, a simple Wiener filter can serve equally well as a deep-learning based deblocker. 
    more » « less
  4. Graphs/Networks are common in real-world applications where data have rich content and complex relationships. The increasing popularity also motivates many network learning algorithms, such as community detection, clustering, classification, and embedding learning, etc.. In reality, the large network volumes often hider a direct use of learning algorithms to the graphs. As a result, it is desirable to have the flexibility to condense a network to an arbitrary size, with well-preserved network topology and node content information. In this paper, we propose a graph compression network (GEN) to achieve network compression and embedding at the same time. Our theme is to leverage the network topology to find node mappings, such that densely connected nodes, including their node content, are compressed as a new node, with a latent vector (i.e. embedding) being learned to represent the compressed node. In addition to compression learning, we also develop a novel encoding-decoding framework, using feature diffusion process, to "decompress" the condensed network. Different from traditional graph convolution which uses direct-neighbor message passing, our decompression advocates high-order message passing within compressed nodes to learning feature representation for all nodes in the network. A unique strength of GEN is that it leverages the graph neural network principle to learn mapping automatically, so one can compress a network to an arbitrary size, and also decompress it to the original node space with minimum information loss. Experiments and comparisons confirm that GEN can automatically find clusters and communities, and compress them as new nodes. Results also show that GEN achieves improved performance for numerous tasks, including graph classification and node clustering. 
    more » « less
  5. The vibration of a compliant panel under a shock / boundary layer interaction (SBLI) induced by a compression ramp in a Mach 2 flow, is investigated experimentally. The panel is made from brass shim stock of length (streamwise), width (spanwise) and thickness of 122 mm by 63.5 mm by 0.25 mm, respectively. The 20° compression ramp is placed near the downstream edge of the compliant panel, and it creates a shock-induced turbulent separated flow that extends over the downstream 20% of the panel. Large pressure fluctuations occur in the region of the separation shock foot unsteadiness. The pressure fluctuations increase vibration amplitudes of the higher panel modes, especially the second mode, which has an antinode near the shock foot region. In this work, the authors use structural modifications of the baseline compliant panel to mitigate vibrations induced by the large pressure fluctuations of the shock foot unsteadiness. A thin rib is attached in the spanwise direction to the lee side of the panel at the location of SBLI. In one configuration, the rib is attached to the panel using epoxy adhesive, which creates a stiff connection. In another configuration, the rib is attached to the panel via double-sided viscoelastic tape, which adds significant damping to the system. The panel vibration and surface pressure field are measured using stereoscopic digital image correlation and pressure sensitive paint. Results show that especially the second vibration mode of the panel is reduced through the addition of the rib. This effect is more pronounced in the case where the viscoelastic tape was used, where a 72% reduction in vibration is observed. 
    more » « less