54 research outputs found
Universal Image Steganalytic Method
In the paper we introduce a new universal steganalytic method in JPEG file format that is detecting well-known and also newly developed steganographic methods. The steganalytic model is trained by MHF-DZ steganographic algorithm previously designed by the same authors. The calibration technique with the Feature Based Steganalysis (FBS) was employed in order to identify statistical changes caused by embedding a secret data into original image. The steganalyzer concept utilizes Support Vector Machine (SVM) classification for training a model that is later used by the same steganalyzer in order to identify between a clean (cover) and steganographic image. The aim of the paper was to analyze the variety in accuracy of detection results (ACR) while detecting testing steganographic algorithms as F5, Outguess, Model Based Steganography without deblocking, JP Hide&Seek which represent the generally used steganographic tools. The comparison of four feature vectors with different lengths FBS (22), FBS (66) FBS(274) and FBS(285) shows promising results of proposed universal steganalytic method comparing to binary methods
A Multi-Algorithm, High Reliability Steganalyzer Based on Services Oriented Architecture
In this prospectus we are proposing to develop a unified Steganalyzer that can not only work with different media types such as images and audio, but further is capable of providing improved accuracy in stego detection through the use of multiple algorithms running in parallel. Our proposed system integrates different steganalysis techniques in a reliable Steganalyzer with distributed and Services Oriented Architecture (SOA). The distributed architecture not only allows for concurrent processing to speed up the system, but also provides higher reliability than reported in the existing literature. The extendable nature of the SOA implementation allows for easy addition of new Steganalysis algorithms to the system in terms of services. The universal steganalysis technique proposed in this prospectus involves two processes; feature extraction and feature classification. Three methods are used for feature extraction; Mel-Cepstrum and Markov (for audio), and Intra-blocks for (JPEG images). The feature classification process is implemented using neural network classifier. The unified steganalyzer is tested for JPEG images and WAV audio files. The accuracy of classification ranges from 96.8% to 99.8% depending on the object type and the feature extraction method. In particular, an enhancement of Mel-Cepstrum technique is proposed that achieves an accuracy of 99.8%. This is significantly better than detection accuracy of 89.9% to 98.6% [Liu 2011] where even a much larger training dataset was used than ours
CNN Based Adversarial Embedding with Minimum Alteration for Image Steganography
Historically, steganographic schemes were designed in a way to preserve image
statistics or steganalytic features. Since most of the state-of-the-art
steganalytic methods employ a machine learning (ML) based classifier, it is
reasonable to consider countering steganalysis by trying to fool the ML
classifiers. However, simply applying perturbations on stego images as
adversarial examples may lead to the failure of data extraction and introduce
unexpected artefacts detectable by other classifiers. In this paper, we present
a steganographic scheme with a novel operation called adversarial embedding,
which achieves the goal of hiding a stego message while at the same time
fooling a convolutional neural network (CNN) based steganalyzer. The proposed
method works under the conventional framework of distortion minimization.
Adversarial embedding is achieved by adjusting the costs of image element
modifications according to the gradients backpropagated from the CNN classifier
targeted by the attack. Therefore, modification direction has a higher
probability to be the same as the sign of the gradient. In this way, the so
called adversarial stego images are generated. Experiments demonstrate that the
proposed steganographic scheme is secure against the targeted adversary-unaware
steganalyzer. In addition, it deteriorates the performance of other
adversary-aware steganalyzers opening the way to a new class of modern
steganographic schemes capable to overcome powerful CNN-based steganalysis.Comment: Submitted to IEEE Transactions on Information Forensics and Securit
Recent Advances in Steganography
Steganography is the art and science of communicating which hides the existence of the communication. Steganographic technologies are an important part of the future of Internet security and privacy on open systems such as the Internet. This book's focus is on a relatively new field of study in Steganography and it takes a look at this technology by introducing the readers various concepts of Steganography and Steganalysis. The book has a brief history of steganography and it surveys steganalysis methods considering their modeling techniques. Some new steganography techniques for hiding secret data in images are presented. Furthermore, steganography in speeches is reviewed, and a new approach for hiding data in speeches is introduced
Steganographer Identification
Conventional steganalysis detects the presence of steganography within single
objects. In the real-world, we may face a complex scenario that one or some of
multiple users called actors are guilty of using steganography, which is
typically defined as the Steganographer Identification Problem (SIP). One might
use the conventional steganalysis algorithms to separate stego objects from
cover objects and then identify the guilty actors. However, the guilty actors
may be lost due to a number of false alarms. To deal with the SIP, most of the
state-of-the-arts use unsupervised learning based approaches. In their
solutions, each actor holds multiple digital objects, from which a set of
feature vectors can be extracted. The well-defined distances between these
feature sets are determined to measure the similarity between the corresponding
actors. By applying clustering or outlier detection, the most suspicious
actor(s) will be judged as the steganographer(s). Though the SIP needs further
study, the existing works have good ability to identify the steganographer(s)
when non-adaptive steganographic embedding was applied. In this chapter, we
will present foundational concepts and review advanced methodologies in SIP.
This chapter is self-contained and intended as a tutorial introducing the SIP
in the context of media steganography.Comment: A tutorial with 30 page
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