Steganography A Data Hiding Technique

Steganography implements an encryption technique in which communication takes place by hiding information. A hidden message is the combination of a secret message with the carrier message. This technique can be used to hide the message in an image, a video file, an audio file or in a file system. There are large variety of steganography techniques that will be used for hiding secret information in images. The final output image is called as a stego-image which consists of a secret message or information. Imperceptibility, payload, and robustness are three most important parameters for audio steganography. For a more secure approach, encryption can be used, which will encrypt the secret message using a secret key and then sent to the receiver. The receiver after receiving the message then decrypts the secret message to obtain the original one. In this paper, compared steganography with cryptography, which is an encrypting technique and explained how steganography provides better security in terms of hiding the secret message. In this paper, the various techniques are illustrated, which are used in steganography and studying the implementation of those techniques. Also, demonstrated the implementation process of one of the steganography techniques. A comparative analysis is performed between various steganographic tools by using the sample test images and test data. The quality metrics such as PSNR and SSIM are calculated for the final output images which are used for rating the tools. This paper also discusses about the Steganalysis which is known as the process of identifying the use of steganography

Review of steganalysis of digital images

Steganography is the science and art of embedding hidden messages into cover multimedia such as text, image, audio and video. Steganalysis is the counterpart of steganography, which wants to identify if there is data hidden inside a digital medium. In this study, some specific steganographic schemes such as HUGO and LSB are studied and the steganalytic schemes developed to steganalyze the hidden message are studied. Furthermore, some new approaches such as deep learning and game theory, which have seldom been utilized in steganalysis before, are studied. In the rest of thesis study some steganalytic schemes using textural features including the LDP and LTP have been implemented

Information Analysis for Steganography and Steganalysis in 3D Polygonal Meshes

Information hiding, which embeds a watermark/message over a cover signal, has recently found extensive applications in, for example, copyright protection, content authentication and covert communication. It has been widely considered as an appealing technology to complement conventional cryptographic processes in the field of multimedia security by embedding information into the signal being protected. Generally, information hiding can be classified into two categories: steganography and watermarking. While steganography attempts to embed as much information as possible into a cover signal, watermarking tries to emphasize the robustness of the embedded information at the expense of embedding capacity. In contrast to information hiding, steganalysis aims at detecting whether a given medium has hidden message in it, and, if possible, recover that hidden message. It can be used to measure the security performance of information hiding techniques, meaning a steganalysis resistant steganographic/watermarking method should be imperceptible not only to Human Vision Systems (HVS), but also to intelligent analysis. As yet, 3D information hiding and steganalysis has received relatively less attention compared to image information hiding, despite the proliferation of 3D computer graphics models which are fairly promising information carriers. This thesis focuses on this relatively neglected research area and has the following primary objectives: 1) to investigate the trade-off between embedding capacity and distortion by considering the correlation between spatial and normal/curvature noise in triangle meshes; 2) to design satisfactory 3D steganographic algorithms, taking into account this trade-off; 3) to design robust 3D watermarking algorithms; 4) to propose a steganalysis framework for detecting the existence of the hidden information in 3D models and introduce a universal 3D steganalytic method under this framework. %and demonstrate the performance of the proposed steganalysis by testing it against six well-known 3D steganographic/watermarking methods. The thesis is organized as follows. Chapter 1 describes in detail the background relating to information hiding and steganalysis, as well as the research problems this thesis will be studying. Chapter 2 conducts a survey on the previous information hiding techniques for digital images, 3D models and other medium and also on image steganalysis algorithms. Motivated by the observation that the knowledge of the spatial accuracy of the mesh vertices does not easily translate into information related to the accuracy of other visually important mesh attributes such as normals, Chapters 3 and 4 investigate the impact of modifying vertex coordinates of 3D triangle models on the mesh normals. Chapter 3 presents the results of an empirical investigation, whereas Chapter 4 presents the results of a theoretical study. Based on these results, a high-capacity 3D steganographic algorithm capable of controlling embedding distortion is also presented in Chapter 4. In addition to normal information, several mesh interrogation, processing and rendering algorithms make direct or indirect use of curvature information. Motivated by this, Chapter 5 studies the relation between Discrete Gaussian Curvature (DGC) degradation and vertex coordinate modifications. Chapter 6 proposes a robust watermarking algorithm for 3D polygonal models, based on modifying the histogram of the distances from the model vertices to a point in 3D space. That point is determined by applying Principal Component Analysis (PCA) to the cover model. The use of PCA makes the watermarking method robust against common 3D operations, such as rotation, translation and vertex reordering. In addition, Chapter 6 develops a 3D specific steganalytic algorithm to detect the existence of the hidden messages embedded by one well-known watermarking method. By contrast, the focus of Chapter 7 will be on developing a 3D watermarking algorithm that is resistant to mesh editing or deformation attacks that change the global shape of the mesh. By adopting a framework which has been successfully developed for image steganalysis, Chapter 8 designs a 3D steganalysis method to detect the existence of messages hidden in 3D models with existing steganographic and watermarking algorithms. The efficiency of this steganalytic algorithm has been evaluated on five state-of-the-art 3D watermarking/steganographic methods. Moreover, being a universal steganalytic algorithm can be used as a benchmark for measuring the anti-steganalysis performance of other existing and most importantly future watermarking/steganographic algorithms. Chapter 9 concludes this thesis and also suggests some potential directions for future work

Non-invasive detection of the electromyographic activity of the deep extrinsic thumb muscles using surface electrodes

Includes bibliographical referencesMotivation: Conventional surface electromyography (EMG) methods cannot be used to detect deep muscle activation. A new non-invasive superficial and deep muscle EMG (sdEMG) technique has recently been used to derive the EMG activity of Brachialis and Tibialis Posterior muscles in the upper and lower limb respectively. The aim of the present study was to apply a modified version of sdEMG to the forearm to detect EMG activity of the deep extrinsic thumb muscles Flexor Pollicis Longus (FPL), Extensor Pollicis Longus (EPL), Extensor Pollicis Brevis (EPB) and Abductor Pollicis Longus (APL) using surface electrodes. Methods: High density monopolar EMG was detected from 2 concentric rings, each consisting of 20 custom designed and manufactured silver electrodes, placed at the distal and proximal thirds of the right forearm of 15 healthy male participants. The EMG signals were recorded by a custom synthesised from open source components, EMG amplifier system interfacing with a custom designed LabVIEW® program. The participants performed 10 repetitions of isometric thumb flexion (TFl), thumb extension (TEx), thumb abduction (TAb), thumb adduction (TAd), index finger flexion (IFFl) and index finger extension (IFEx). Each isometric contraction was performed in a randomized order at a standardized effort level of 30% of the participant's maximum voluntary contraction (verified by a custom designed and built thumb dynamometer). The Independent Component Analysis (ICA) algorithm, fastICA, was used to un-mix the 40 monopolar EMG waveforms (containing EMG activity attributable to both superficial and deep muscles) into 40 constitutive components, known as the Independent Components (ICs). The activation envelope of the ICs was found using a 250ms RMS smoothing filter and normalized between 0 and 1. A contraction sequence specific predicted EMG waveform based on intramuscular measurements (from existing studies in the literature) was created for each deep muscle and correlated with the processed ICs using Pearson's Correlation Coefficient (r). The ICs were ranked according to the corresponding r value and the highest r ranked IC for each muscle was considered to represent the recovered EMG activity from that particular muscle. Finally, a per sample basis accuracy, sensitivity and specificity analysis was conducted between each deep muscle's predicted EMG and highest r ranked IC at different activation thresholds. A linear mixed-effects statistical model was used to find the overall accuracy, sensitivity and specificity values over all the thresholds for each deep muscle. Results: Overall correlations of 0.81 for FPL (D), 0.88 for EPL (D), 0.92 for EPB (D) and 0.83 for APL (D) (p<0.001 for all muscles) were found between the predicted EMG waveforms and ICs. Using an activation threshold of 3 standard deviations above a resting baseline level, statistically significant (p<0.001) accuracy, sensitivity and specificity measures were found between the predicted EMG waveforms and top r ranked ICs for each of the deep muscles. The values of the 3 statistical measures (accuracy, sensitivity, specificity) for each of the deep muscles were: FPL (0.76, 0.88, 0.70); EPL (0.87, 0.85, 0.91); EPB (0.94, 0.93, 0.94); APL (0.80, 0.87, 0.87). Conclusions: The results indicate that this is the first non-invasive detection of the EMG activity of FPL (D), EPL (D), EPB (D) and APL (D). The ability to detect movement intention as a result of activation from these muscles may be of use for robot based targeted rehabilitation of the hand or in the control of prosthetic hand devices