16 research outputs found
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
Limits of Reliable Communication with Low Probability of Detection on AWGN Channels
We present a square root limit on the amount of information transmitted
reliably and with low probability of detection (LPD) over additive white
Gaussian noise (AWGN) channels. Specifically, if the transmitter has AWGN
channels to an intended receiver and a warden, both with non-zero noise power,
we prove that bits can be sent from the transmitter to the
receiver in channel uses while lower-bounding
for any , where and respectively denote the
warden's probabilities of a false alarm when the sender is not transmitting and
a missed detection when the sender is transmitting. Moreover, in most practical
scenarios, a lower bound on the noise power on the channel between the
transmitter and the warden is known and bits can be sent in
LPD channel uses. Conversely, attempting to transmit more than
bits either results in detection by the warden with probability one or a
non-zero probability of decoding error at the receiver as .Comment: Major revision in v2. Context, esp. the relationship to steganography
updated. Also, added discussion on secret key length. Results are unchanged
from previous version. Minor revision in v3. Major revision in v4, Clarified
derivations (adding appendix), also context, esp. relationship to previous
work in communication updated. Results are unchanged from previous revision
Dynamic hashing technique for bandwidth reduction in image transmission
Hash functions are widely used in secure communication systems by generating the message digests for detection of unauthorized changes in the files. Encrypted hashed message or digital signature is used in many applications like authentication to ensure data integrity. It is almost impossible to ensure authentic messages when sending over large bandwidth in highly accessible network especially on insecure channels. Two issues that required to be addressed are the large size of hashed message and high bandwidth. A collaborative approach between encoded hash message and steganography provides a highly secure hidden data. The aim of the research is to propose a new method for producing a dynamic and smaller encoded hash message with reduced bandwidth. The encoded hash message is embedded into an image as a stego-image to avoid additional file and consequently the bandwidth is reduced. The receiver extracts the encoded hash and dynamic hashed message from the received file at the same time. If decoding encrypted hash by public key and hashed message from the original file matches the received file, it is considered as authentic. In enhancing the robustness of the hashed message, we compressed or encoded it or performed both operations before embedding the hashed data into the image. The proposed algorithm had achieved the lowest dynamic size (1 KB) with no fix length of the original file compared to MD5, SHA-1 and SHA-2 hash algorithms. The robustness of hashed message was tested against the substitution, replacement and collision attacks to check whether or not there is any detection of the same message in the output. The results show that the probability of the existence of the same hashed message in the output is closed to 0% compared to the MD5 and SHA algorithms. Amongst the benefits of this proposed algorithm is computational efficiency, and for messages with the sizes less than 1600 bytes, the hashed file reduced the original file up to 8.51%
Optimization of medical image steganography using n-decomposition genetic algorithm
Protecting patients' confidential information is a critical concern in medical image steganography. The Least Significant Bits (LSB) technique has been widely used for secure communication. However, it is susceptible to imperceptibility and security risks due to the direct manipulation of pixels, and ASCII patterns present limitations. Consequently, sensitive medical information is subject to loss or alteration. Despite attempts to optimize LSB, these issues persist due to (1) the formulation of the optimization suffering from non-valid implicit constraints, causing inflexibility in reaching optimal embedding, (2) lacking convergence in the searching process, where the message length significantly affects the size of the solution space, and (3) issues of application customizability where different data require more flexibility in controlling the embedding process. To overcome these limitations, this study proposes a technique known as an n-decomposition genetic algorithm. This algorithm uses a variable-length search to identify the best location to embed the secret message by incorporating constraints to avoid local minimum traps. The methodology consists of five main phases: (1) initial investigation, (2) formulating an embedding scheme, (3) constructing a decomposition scheme, (4) integrating the schemes' design into the proposed technique, and (5) evaluating the proposed technique's performance based on parameters using medical datasets from kaggle.com. The proposed technique showed resistance to statistical analysis evaluated using Reversible Statistical (RS) analysis and histogram. It also demonstrated its superiority in imperceptibility and security measured by MSE and PSNR to Chest and Retina datasets (0.0557, 0.0550) and (60.6696, 60.7287), respectively. Still, compared to the results obtained by the proposed technique, the benchmark outperforms the Brain dataset due to the homogeneous nature of the images and the extensive black background. This research has contributed to genetic-based decomposition in medical image steganography and provides a technique that offers improved security without compromising efficiency and convergence. However, further validation is required to determine its effectiveness in real-world applications
The square root law of steganographic capacity for Markov covers.
It is a well-established result that steganographic capacity of perfectly secure stegosystems grows linearly with the number of cover elements-secure steganography has a positive rate. In practice, however, neither the Warden nor the Steganographer has perfect knowledge of the cover source and thus it is unlikely that perfectly secure stegosystems for complex covers, such as digital media, will ever be constructed. This justifies study of secure capacity of imperfect stegosystems. Recent theoretical results from batch steganography, supported by experiments with blind steganalyzers, point to an emerging paradigm: whether steganography is performed in a large batch of cover objects or a single large object, there is a wide range of practical situations in which secure capacity rate is vanishing. In particular, the absolute size of secure payload appears to only grow with the square root of the cover size. In this paper, we study the square root law of steganographic capacity and give a formal proof of this law for imperfect stegosystems, assuming that the cover source is a stationary Markov chain and the embedding changes are mutually independent. © 2009 Copyright SPIE - The International Society for Optical Engineering