194 research outputs found

    Micro protocol engineering for unstructured carriers: On the embedding of steganographic control protocols into audio transmissions

    Full text link
    Network steganography conceals the transfer of sensitive information within unobtrusive data in computer networks. So-called micro protocols are communication protocols placed within the payload of a network steganographic transfer. They enrich this transfer with features such as reliability, dynamic overlay routing, or performance optimization --- just to mention a few. We present different design approaches for the embedding of hidden channels with micro protocols in digitized audio signals under consideration of different requirements. On the basis of experimental results, our design approaches are compared, and introduced into a protocol engineering approach for micro protocols.Comment: 20 pages, 7 figures, 4 table

    Using Transcoding for Hidden Communication in IP Telephony

    Get PDF
    The paper presents a new steganographic method for IP telephony called TranSteg (Transcoding Steganography). Typically, in steganographic communication it is advised for covert data to be compressed in order to limit its size. In TranSteg it is the overt data that is compressed to make space for the steganogram. The main innovation of TranSteg is to, for a chosen voice stream, find a codec that will result in a similar voice quality but smaller voice payload size than the originally selected. Then, the voice stream is transcoded. At this step the original voice payload size is intentionally unaltered and the change of the codec is not indicated. Instead, after placing the transcoded voice payload, the remaining free space is filled with hidden data. TranSteg proof of concept implementation was designed and developed. The obtained experimental results are enclosed in this paper. They prove that the proposed method is feasible and offers a high steganographic bandwidth. TranSteg detection is difficult to perform when performing inspection in a single network localisation.Comment: 17 pages, 16 figures, 4 table

    Unified Description for Network Information Hiding Methods

    Full text link
    Until now hiding methods in network steganography have been described in arbitrary ways, making them difficult to compare. For instance, some publications describe classical channel characteristics, such as robustness and bandwidth, while others describe the embedding of hidden information. We introduce the first unified description of hiding methods in network steganography. Our description method is based on a comprehensive analysis of the existing publications in the domain. When our description method is applied by the research community, future publications will be easier to categorize, compare and extend. Our method can also serve as a basis to evaluate the novelty of hiding methods proposed in the future.Comment: 24 pages, 7 figures, 1 table; currently under revie

    The Automation of the Extraction of Evidence masked by Steganographic Techniques in WAV and MP3 Audio Files

    Full text link
    Antiforensics techniques and particularly steganography and cryptography have become increasingly pressing issues that affect the current digital forensics practice, both techniques are widely researched and developed as considered in the heart of the modern digital era but remain double edged swords standing between the privacy conscious and the criminally malicious, dependent on the severity of the methods deployed. This paper advances the automation of hidden evidence extraction in the context of audio files enabling the correlation between unprocessed evidence artefacts and extreme Steganographic and Cryptographic techniques using the Least Significant Bits extraction method (LSB). The research generates an in-depth review of current digital forensic toolkit and systems and formally address their capabilities in handling steganography-related cases, we opted for experimental research methodology in the form of quantitative analysis of the efficiency of detecting and extraction of hidden artefacts in WAV and MP3 audio files by comparing standard industry software. This work establishes an environment for the practical implementation and testing of the proposed approach and the new toolkit for extracting evidence hidden by Cryptographic and Steganographic techniques during forensics investigations. The proposed multi-approach automation demonstrated a huge positive impact in terms of efficiency and accuracy and notably on large audio files (MP3 and WAV) which the forensics analysis is time-consuming and requires significant computational resources and memory. However, the proposed automation may occasionally produce false positives (detecting steganography where none exists) or false negatives (failing to detect steganography that is present) but overall achieve a balance between detecting hidden data accurately along with minimising the false alarms.Comment: Wires Forensics Sciences Under Revie

    Universal Image Steganalytic Method

    Get PDF
    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

    Steganographic Generative Adversarial Networks

    Full text link
    Steganography is collection of methods to hide secret information ("payload") within non-secret information "container"). Its counterpart, Steganalysis, is the practice of determining if a message contains a hidden payload, and recovering it if possible. Presence of hidden payloads is typically detected by a binary classifier. In the present study, we propose a new model for generating image-like containers based on Deep Convolutional Generative Adversarial Networks (DCGAN). This approach allows to generate more setganalysis-secure message embedding using standard steganography algorithms. Experiment results demonstrate that the new model successfully deceives the steganography analyzer, and for this reason, can be used in steganographic applications.Comment: 15 pages, 10 figures, 5 tables, Workshop on Adversarial Training (NIPS 2016, Barcelona, Spain

    An Analysis of Perturbed Quantization Steganography in the Spatial Domain

    Get PDF
    Steganography is a form of secret communication in which a message is hidden into a harmless cover object, concealing the actual existence of the message. Due to the potential abuse by criminals and terrorists, much research has also gone into the field of steganalysis - the art of detecting and deciphering a hidden message. As many novel steganographic hiding algorithms become publicly known, researchers exploit these methods by finding statistical irregularities between clean digital images and images containing hidden data. This creates an on-going race between the two fields and requires constant countermeasures on the part of steganographers in order to maintain truly covert communication. This research effort extends upon previous work in perturbed quantization (PQ) steganography by examining its applicability to the spatial domain. Several different information-reducing transformations are implemented along with the PQ system to study their effect on the security of the system as well as their effect on the steganographic capacity of the system. Additionally, a new statistical attack is formulated for detecting ± 1 embedding techniques in color images. Results from performing state-of-the-art steganalysis reveal that the system is less detectable than comparable hiding methods. Grayscale images embedded with message payloads of 0.4bpp are detected only 9% more accurately than by random guessing, and color images embedded with payloads of 0.2bpp are successfully detected only 6% more reliably than by random guessing
    corecore