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

Survey of the Use of Steganography over the Internet

This paper addressesthe use of Steganography over the Internet by terrorists. There were ru-mors in the newspapers that Steganography is being used to covert communication between terrorists, without presenting any scientific proof. Niels Provos and Peter Honeyman conducted an extensive Internet search where they analyzed over 2 million images and didn’t find a single hidden image. After this study the scientific community was divided: some believed that Niels Provos and Peter Honeyman was conclusive enough other did not. This paper describes what Steganography is and what can be used for, various Steganography techniques and also presents the studies made regarding the use of Steganography on the Internet.Steganography, Secret Communication, Information Hiding, Cryptography

A Session based Multiple Image Hiding Technique using DWT and DCT

This work proposes Steganographic technique for hiding multiple images in a color image based on DWT and DCT. The cover image is decomposed into three separate color planes namely R, G and B. Individual planes are decomposed into subbands using DWT. DCT is applied in HH component of each plane. Secret images are dispersed among the selected DCT coefficients using a pseudo random sequence and a Session key. Secret images are extracted using the session key and the size of the images from the planer decomposed stego image. In this approach the stego image generated is of acceptable level of imperceptibility and distortion compared to the cover image and the overall security is high.Comment: 4 pages,16 figures, "Published with International Journal of Computer Applications (IJCA)

Hybrid Cryptography and Steganography-Based Security System for IoT Networks

Despite the fact that many IoT devices are publicly accessible to everyone on the network, understanding the security risks and threats posed by cyber attacks is critical; as a result, it should be safeguarded. Plain text is constructed into encrypted text, before being delivered by using cryptography, and is then reconstructed back to plain text after receiving a response from the recipient. The steganography technique can be used to hide sensitive information incorporated in a text, audio, or video file. One approach is to hide data in bits that correspond to successive rows of pixels with the same color in an image file.  As a consequence, the image file retains the original's appearance while also containing "noise" patterns made out of common, unencrypted data. To do this, the encrypted data is subtly applied to the redundant data. In this work, it is suggested that IoT network data be encrypted using cryptography, and that an encrypted message be concealed inside an image file using steganography. Additionally, it is suggested to enhance the number of bits that may be stored within a single picture pixel.  The payload that may be sent through an image is significantly increased by incorporating Convolutional Neural Networks into the classic steganography technique. In this work, we propose, design, and train Convolutional Neural Networks (CNN) to enhance the amount of data that can be securely encrypted and decrypted to show the original message

A High Secured Steganalysis using QVDHC Model

Data compression plays a vital role in data security as it saves memory, transfer speed is high, easy to handle and secure. Mainly the compression techniques are categorized into two types. They are lossless, lossy data compression. The data format will be an audio, image, text or video. The main objective is to save memory of using these techniques is to save memory and to preserve data confidentiality, integrity. In this paper, a hybrid approach was proposed which combines Quotient Value Difference (QVD) with Huffman coding. These two methods are more efficient, simple to implement and provides better security to the data. The secret message is encoded using Huffman coding, while the cover image is compressed using QVD. Then the encoded data is embedded into cover image and transferred over the network to receiver. At the receiver end, the data is decompressed to obtain original message. The proposed method shows high level performance when compared to other existing methods with better quality and minimum error

"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