An Adaptive Steganography Scheme Based on Visual Quality and Embedding Capacity Improvement

In this paper, a steganography technique using LSB substitution and PVD method is presented as an adaptive scheme in the spatial domain. Our method partitions the grayscale image into several non-overlapping blocks with three consecutive pixels. The embedding algorithm can both replace the secret data with the LSBs of the middle pixel and embed it in the difference values between the middle pixel and its two neighboring pixels of the cover-block. The number of secret bits is determined adaptively based on the range divisions for embedding in the difference value. We define a new range division on gray level which takes into account a larger embedding capacity for bits. After the embedding, the proposed method detects the pixels which are sensitive to hyper distortion. Then, the embedding process will be repeated to produce insignificant visual distortion in those pixels. Our experimental results demonstrate that this iterative steganography scheme prevents significant visual distortion into stego-image. The generated PSNR values are higher than the corresponding values of the most commonly used methods, discussed in this study. Furthermore, the experimental results show that the hiding capacity increased enormously when the proposed range division is used. Finally, we illustrate that the method can pass RS and steganalysis detector attacks.DOI:http://dx.doi.org/10.11591/ijece.v4i4.630

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

Side-Information For Steganography Design And Detection

Today, the most secure steganographic schemes for digital images embed secret messages while minimizing a distortion function that describes the local complexity of the content. Distortion functions are heuristically designed to predict the modeling error, or in other words, how difficult it would be to detect a single change to the original image in any given area. This dissertation investigates how both the design and detection of such content-adaptive schemes can be improved with the use of side-information. We distinguish two types of side-information, public and private: Public side-information is available to the sender and at least in part also to anybody else who can observe the communication. Content complexity is a typical example of public side-information. While it is commonly used for steganography, it can also be used for detection. In this work, we propose a modification to the rich-model style feature sets in both spatial and JPEG domain to inform such feature sets of the content complexity. Private side-information is available only to the sender. The previous use of private side-information in steganography was very successful but limited to steganography in JPEG images. Also, the constructions were based on heuristic with little theoretical foundations. This work tries to remedy this deficiency by introducing a scheme that generalizes the previous approach to an arbitrary domain. We also put forward a theoretical investigation of how to incorporate side-information based on a model of images. Third, we propose to use a novel type of side-information in the form of multiple exposures for JPEG steganography

Is ensemble classifier needed for steganalysis in high-dimensional feature spaces?

International audienceThe ensemble classifier, based on Fisher Linear Discriminant base learners, was introduced specifically for steganalysis of digital media, which currently uses high-dimensional feature spaces. Presently it is probably the most used method to design supervised classifier for steganalysis of digital images because of its good detection accuracy and small computational cost. It has been assumed by the community that the classifier implements a non-linear boundary through pooling binary decision of individual classifiers within the ensemble. This paper challenges this assumption by showing that linear classifier obtained by various regularizations of the FLD can perform equally well as the ensemble. Moreover it demonstrates that using state of the art solvers linear classifiers can be trained more efficiently and offer certain potential advantages over the original ensemble leading to much lower computational complexity than the ensemble classifier. All claims are supported experimentally on a wide spectrum of stego schemes operating in both the spatial and JPEG domains with a multitude of rich steganalysis feature sets

Convolutional Neural Networks for Image Steganalysis in the Spatial Domain

Esta tesis doctoral muestra los resultados obtenidos al aplicar Redes Neuronales Convolucionales (CNNs) para el estegoanálisis de imágenes digitales en el dominio espacial. La esteganografía consiste en ocultar mensajes dentro de un objeto conocido como portador para establecer un canal de comunicación encubierto para que el acto de comunicación pase desapercibido para los observadores que tienen acceso a ese canal. Steganalysis se dedica a detectar mensajes ocultos mediante esteganografía; estos mensajes pueden estar implícitos en diferentes tipos de medios, como imágenes digitales, archivos de video, archivos de audio o texto sin formato. Desde 2014, los investigadores se han interesado especialmente en aplicar técnicas de Deep Learning (DL) para lograr resultados que superen los métodos tradicionales de Machine Learning (ML).Is doctoral thesis shows the results obtained by applying Convolutional Neural Networks (CNNs) for the steganalysis of digital images in the spatial domain. Steganography consists of hiding messages inside an object known as a carrier to establish a covert communication channel so that the act of communication goes unnoticed by observers who have access to that channel. Steganalysis is dedicated to detecting hidden messages using steganography; these messages can be implicit in di.erent types of media, such as digital images, video €les, audio €les, or plain text. Since 2014 researchers have taken a particular interest in applying Deep Learning (DL) techniques to achieving results that surpass traditional Machine Learning (ML) methods

Color image steganalysis based on quaternion discrete cosine transform

With the rapid development and application of Internet technology in recent years, the issue of information security has received more and more attention. Digital steganography is used as a means of secure communication to hide information by modifying the carrier. However, steganography can also be used for illegal acts, so it is of great significance to study steganalysis techniques. The steganalysis technology can be used to solve the illegal steganography problem of computer vision and engineering applications technology. Most of the images in the Internet are color images, and steganalysis for color images is a very critical problem in the field of steganalysis at this stage. Currently proposed algorithms for steganalysis of color images mainly rely on the manual design of steganographic features, and the steganographic features do not fully consider the internal connection between the three channels of color images. In recent years, advanced steganography techniques for color images have been proposed, which brings more serious challenges to color image steganalysis. Quaternions are a good tool to represent color images, and the transformation of quaternions can fully exploit the correlation among color image channels. In this paper, we propose a color image steganalysis algorithm based on quaternion discrete cosine transform, firstly, the image is represented by quaternion, then the quaternion discrete cosine transform is applied to it, and the coefficients obtained from the transformation are extracted to design features of the coeval matrix. The experimental results show that the proposed algorithm works better than the typical color image steganalysis algorithm