An Artificial Neural Network for Wavelet Steganalysis

Hiding messages in image data, called steganography, is used for both legal and illicit purposes. The detection of hidden messages in image data stored on websites and computers, called steganalysis, is of prime importance to cyber forensics personnel. Automating the detection of hidden messages is a requirement, since the shear amount of image data stored on computers or websites makes it impossible for a person to investigate each image separately. This paper describes research on a prototype software system that automatically classifies an image as having hidden information or not, using a sophisticated artificial neural network (ANN) system. An ANN software package, the ISU ACL NetWorks Toolkit, is trained on a selection of image features that distinguish between stego and nonstego images. The novelty of this ANN is that it is a blind classifier that gives more accurate results than previous systems. It can detect messages hidden using a variety of different types of embedding algorithms. A Graphical User Interface (GUI) combines the ANN, feature selection, and embedding algorithms into a prototype software package that is not currently available to the cyber forensics community

Hunting wild stego images, a domain adaptation problem in digital image forensics

Digital image forensics is a field encompassing camera identication, forgery detection and steganalysis. Statistical modeling and machine learning have been successfully applied in the academic community of this maturing field. Still, large gaps exist between academic results and applications used by practicing forensic analysts, especially when the target samples are drawn from a different population than the data in a reference database. This thesis contains four published papers aiming at narrowing this gap in three different fields: mobile stego app detection, digital image steganalysis and camera identification. It is the first work to explore a way of extending the academic methods to real world images created by apps. New ideas and methods are developed for target images with very rich flexibility in the embedding rates, embedding algorithms, exposure settings and camera sources. The experimental results proved that the proposed methods work very well, even for the devices which are not included in the reference database

Enhancing Biometric Security: A Framework for Detecting and Preventing False Identification

Biometrics is a technological system that utilizes data to differentiate one individual from another. The biometric framework can be used by government and private organizations for security purposes. This software-based technology helps to look at an individual's data if it is genuine or fake. The study suggested a framework; its goal is to strengthen the development and acceptance of the biometric system. The function of this system is to reduce the applied effort to identify and recognize the quality of the image in less time. This study utilizes three data applications: iris, fingerprint, and face recognition. The approach proposed by the survey uses different features of the images to determine the difference between the original image and the considered sample image. It gives efficient protection against different spoofing attacks. Simulation results show that the high-quality detection application has an average peak signal-to-noise ratio (PNSR) of 89.77. Further, the proposed model effectively detects false biometric identification

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

Improved steganalysis technique based on least significant bit using artificial neural network for MP3 files

MP3 files are one of the most widely used digital audio formats that provide a high compression ratio with reliable quality. Their widespread use has resulted in MP3 audio files becoming excellent covers to carry hidden information in audio steganography on the Internet. Emerging interest in uncovering such hidden information has opened up a field of research called steganalysis that looked at the detection of hidden messages in a specific media. Unfortunately, the detection accuracy in steganalysis is affected by bit rates, sampling rate of the data type, compression rates, file track size and standard, as well as benchmark dataset of the MP3 files. This thesis thus proposed an effective technique to steganalysis of MP3 audio files by deriving a combination of features from MP3 file properties. Several trials were run in selecting relevant features of MP3 files like the total harmony distortion, power spectrum density, and peak signal-to-noise ratio (PSNR) for investigating the correlation between different channels of MP3 signals. The least significant bit (LSB) technique was used in the detection of embedded secret files in stego-objects. This involved reading the stego-objects for statistical evaluation for possible points of secret messages and classifying these points into either high or low tendencies for containing secret messages. Feed Forward Neural Network with 3 layers and traingdx function with an activation function for each layer were also used. The network vector contains information about all features, and is used to create a network for the given learning process. Finally, an evaluation process involving the ANN test that compared the results with previous techniques, was performed. A 97.92% accuracy rate was recorded when detecting MP3 files under 96 kbps compression. These experimental results showed that the proposed approach was effective in detecting embedded information in MP3 files. It demonstrated significant improvement in detection accuracy at low embedding rates compared with previous work

Data Hiding in Gray-Scale Images by LSB Method using IWT with Lifting Scheme

This paper introduced a completely unique steganography technique to extend the capability and therefore the physical property of the image once embedding. Genetic rule utilized to get associate degree optimum mapping operate to minimize the error distinction between the quilt and therefore the stego image and use the block mapping technique to preserve the native image properties. Additionally we have a tendency to applied the OPAP to extend the activi ty capability of the rule comp are d to different systems. However, the process complexity of the new rule is high. The simulation results showed that capability and physical property of image had enl arg ed timing. Also, we will choose the most effective blo ck size to scale back the computation value and to extend the PSNR victimisation optimisation algorithms like GA

Unified Description for Network Information Hiding Methods

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

Multilayer Reversible Data Hiding Via Histogram Shifting

Concealing messages from unauthorised people has been desired since written communication first began. With advancements in digital communication technology and the growth of computer power and storage, the difficulty of ensuring the privacy of individuals and the protection of copyright has become increasingly challenging. Steganography finds a role in attempting to address these growing concerns. Problems arise in the steganography method because of the trade-off between capacity and imperceptibility whereby increasing the embedding capacity increases the distortion in the stego object and it thus becomes suspect. Another problem is concerned with non-retrieval of the original cover object whereby misplacing data could be crucial for example in the case of medical images. Reversible data hiding technique based on histogram shifting addresses the problem of retrieving the original cover. Embedding the secret message by shifting the histogram between the pair of the peak and minimum points wastes the embedding capacity and does not control the distortion in the stego image for various secret messages sizes. In this research, a technique for reversible data hiding is proposed which enables the retrieval of both the hidden secret message and the original image at the receiver’s side. The proposed technique considers the size of the secret message and the distribution of the colour values within the cover image to determine the value of the optimal pair or set of container and carried colours within the best sub image instead of the pair of peak and minimum points. The experimental results show that the proposed technique increases the embedding capacity within the cover image and produces a stego image with a high peak signal-to-noise ratio value. In addition, the experimental results show that by using the proposed re-shifting and extraction formulas, the technique has the ability to extract the hidden data and retrieve the original images from the stego images. In comparison to the traditional histogram-shifting techniques, the proposed technique significantly improves the stego image quality and the embedding capacity. Thus, this research has contributed to two principles, namely improvements in capacity and quality