Learning Iterative Neural Optimizers for Image Steganography

Image steganography is the process of concealing secret information in images through imperceptible changes. Recent work has formulated this task as a classic constrained optimization problem. In this paper, we argue that image steganography is inherently performed on the (elusive) manifold of natural images, and propose an iterative neural network trained to perform the optimization steps. In contrast to classical optimization methods like L-BFGS or projected gradient descent, we train the neural network to also stay close to the manifold of natural images throughout the optimization. We show that our learned neural optimization is faster and more reliable than classical optimization approaches. In comparison to previous state-of-the-art encoder-decoder-based steganography methods, it reduces the recovery error rate by multiple orders of magnitude and achieves zero error up to 3 bits per pixel (bpp) without the need for error-correcting codes.Comment: International Conference on Learning Representations (ICLR) 202

"The Good, The Bad And The Ugly": Evaluation of Wi-Fi Steganography

In this paper we propose a new method for the evaluation of network steganography algorithms based on the new concept of "the moving observer". We considered three levels of undetectability named: "good", "bad", and "ugly". To illustrate this method we chose Wi-Fi steganography as a solid family of information hiding protocols. We present the state of the art in this area covering well-known hiding techniques for 802.11 networks. "The moving observer" approach could help not only in the evaluation of steganographic algorithms, but also might be a starting point for a new detection system of network steganography. The concept of a new detection system, called MoveSteg, is explained in detail.Comment: 6 pages, 6 figures, to appear in Proc. of: ICNIT 2015 - 6th International Conference on Networking and Information Technology, Tokyo, Japan, November 5-6, 201

A New Blind Method for Detecting Novel Steganography

Steganography is the art of hiding a message in plain sight. Modern steganographic tools that conceal data in innocuous-looking digital image files are widely available. The use of such tools by terrorists, hostile states, criminal organizations, etc., to camouflage the planning and coordination of their illicit activities poses a serious challenge. Most steganography detection tools rely on signatures that describe particular steganography programs. Signature-based classifiers offer strong detection capabilities against known threats, but they suffer from an inability to detect previously unseen forms of steganography. Novel steganography detection requires an anomaly-based classifier. This paper describes and demonstrates a blind classification algorithm that uses hyper-dimensional geometric methods to model steganography-free jpeg images. The geometric model, comprising one or more convex polytopes, hyper-spheres, or hyper-ellipsoids in the attribute space, provides superior anomaly detection compared to previous research. Experimental results show that the classifier detects, on average, 85.4% of Jsteg steganography images with a mean embedding rate of 0.14 bits per pixel, compared to previous research that achieved a mean detection rate of just 65%. Further, the classification algorithm creates models for as many training classes of data as are available, resulting in a hybrid anomaly/signature or signature-only based classifier, which increases Jsteg detection accuracy to 95%

The Importance of Generalizability to Anomaly Detection

In security-related areas there is concern over novel “zero-day” attacks that penetrate system defenses and wreak havoc. The best methods for countering these threats are recognizing “nonself” as in an Artificial Immune System or recognizing “self” through clustering. For either case, the concern remains that something that appears similar to self could be missed. Given this situation, one could incorrectly assume that a preference for a tighter fit to self over generalizability is important for false positive reduction in this type of learning problem. This article confirms that in anomaly detection as in other forms of classification a tight fit, although important, does not supersede model generality. This is shown using three systems each with a different geometric bias in the decision space. The first two use spherical and ellipsoid clusters with a k-means algorithm modified to work on the one-class/blind classification problem. The third is based on wrapping the self points with a multidimensional convex hull (polytope) algorithm capable of learning disjunctive concepts via a thresholding constant. All three of these algorithms are tested using the Voting dataset from the UCI Machine Learning Repository, the MIT Lincoln Labs intrusion detection dataset, and the lossy-compressed steganalysis domain

Tackling Android Stego Apps in the Wild

Digital image forensics is a young but maturing field, encompassing key areas such as camera identification, detection of forged images, and steganalysis. However, large gaps exist between academic results and applications used by practicing forensic analysts. To move academic discoveries closer to real-world implementations, it is important to use data that represent "in the wild" scenarios. For detection of stego images created from steganography apps, images generated from those apps are ideal to use. In this paper, we present our work to perform steg detection on images from mobile apps using two different approaches: "signature" detection, and machine learning methods. A principal challenge of the ML task is to create a great many of stego images from different apps with certain embedding rates. One of our main contributions is a procedure for generating a large image database by using Android emulators and reverse engineering techniques. We develop algorithms and tools for signature detection on stego apps, and provide solutions to issues encountered when creating ML classifiers

Fingerprinted secret sharing steganography for robustness against image cropping attacks

Steganography is the art and science of hiding information. In this paper we propose a conceptual framework for Fingerprinted Secret Sharing Steganography. We offer a technique to break the main secret into multiple parts and hide them individually in a cover medium. We use a novel technique to compress the data to a considerable extent. We use the Lagrange Interpolating Polynomial method to recover the shared secret. We also show how the proposed technique can offer robust mechanism to protect data loss because of image cropping. We use the (k,n) threshold scheme to decide the minimum number of parts required to recover the secret data completely. The security of our scheme is based on the security principle of steganography and secret sharing scheme