Generating Steganographic Images via Adversarial Training

Adversarial training was recently shown to be competitive against supervised learning methods on computer vision tasks, however, studies have mainly been confined to generative tasks such as image synthesis. In this paper, we apply adversarial training techniques to the discriminative task of learning a steganographic algorithm. Steganography is a collection of techniques for concealing information by embedding it within a non-secret medium, such as cover texts or images. We show that adversarial training can produce robust steganographic techniques: our unsupervised training scheme produces a steganographic algorithm that competes with state-of-the-art steganographic techniques, and produces a robust steganalyzer, which performs the discriminative task of deciding if an image contains secret information. We define a game between three parties, Alice, Bob and Eve, in order to simultaneously train both a steganographic algorithm and a steganalyzer. Alice and Bob attempt to communicate a secret message contained within an image, while Eve eavesdrops on their conversation and attempts to determine if secret information is embedded within the image. We represent Alice, Bob and Eve by neural networks, and validate our scheme on two independent image datasets, showing our novel method of studying steganographic problems is surprisingly competitive against established steganographic techniques

Deep Learning for Reversible Steganography: Principles and Insights

Deep-learning\textendash{centric} reversible steganography has emerged as a promising research paradigm. A direct way of applying deep learning to reversible steganography is to construct a pair of encoder and decoder, whose parameters are trained jointly, thereby learning the steganographic system as a whole. This end-to-end framework, however, falls short of the reversibility requirement because it is difficult for this kind of monolithic system, as a black box, to create or duplicate intricate reversible mechanisms. In response to this issue, a recent approach is to carve up the steganographic system and work on modules independently. In particular, neural networks are deployed in an analytics module to learn the data distribution, while an established mechanism is called upon to handle the remaining tasks. In this paper, we investigate the modular framework and deploy deep neural networks in a reversible steganographic scheme referred to as prediction-error modulation, in which an analytics module serves the purpose of pixel intensity prediction. The primary focus of this study is on deep-learning\textendash{based} context-aware pixel intensity prediction. We address the unsolved issues reported in related literature, including the impact of pixel initialisation on prediction accuracy and the influence of uncertainty propagation in dual-layer embedding. Furthermore, we establish a connection between context-aware pixel intensity prediction and low-level computer vision and analyse the performance of several advanced neural networks

Towards private and robust machine learning for information security

Many problems in information security are pattern recognition problems. For example, determining if a digital communication can be trusted amounts to certifying that the communication does not carry malicious or secret content, which can be distilled into the problem of recognising the difference between benign and malicious content. At a high level, machine learning is the study of how patterns are formed within data, and how learning these patterns generalises beyond the potentially limited data pool at a practitioner’s disposal, and so has become a powerful tool in information security. In this work, we study the benefits machine learning can bring to two problems in information security. Firstly, we show that machine learning can be used to detect which websites are visited by an internet user over an encrypted connection. By analysing timing and packet size information of encrypted network traffic, we train a machine learning model that predicts the target website given a stream of encrypted network traffic, even if browsing is performed over an anonymous communication network. Secondly, in addition to studying how machine learning can be used to design attacks, we study how it can be used to solve the problem of hiding information within a cover medium, such as an image or an audio recording, which is commonly referred to as steganography. How well an algorithm can hide information within a cover medium amounts to how well the algorithm models and exploits areas of redundancy. This can again be reduced to a pattern recognition problem, and so we apply machine learning to design a steganographic algorithm that efficiently hides a secret message with an image. Following this, we proceed with discussions surrounding why machine learning is not a panacea for information security, and can be an attack vector in and of itself. We show that machine learning can leak private and sensitive information about the data it used to learn, and how malicious actors can exploit vulnerabilities in these learning algorithms to compel them to exhibit adversarial behaviours. Finally, we examine the problem of the disconnect between image recognition systems learned by humans and by machine learning models. While human classification of an image is relatively robust to noise, machine learning models do not possess this property. We show how an attacker can cause targeted misclassifications against an entire data distribution by exploiting this property, and go onto introduce a mitigation that ameliorates this undesirable trait of machine learning

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

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

The role of side information in steganography

Das Ziel digitaler Steganographie ist es, eine geheime Kommunikation in digitalen Medien zu verstecken. Der übliche Ansatz ist es, die Nachricht in einem empirischen Trägermedium zu verstecken. In dieser Arbeit definieren wir den Begriff der Steganographischen Seiteninformation (SSI). Diese Definition umfasst alle wichtigen Eigenschaften von SSI. Wir begründen die Definition informationstheoretisch und erklären den Einsatz von SSI. Alle neueren steganographischen Algorithmen nutzen SSI um die Nachricht einzubetten. Wir entwickeln einen Angriff auf adaptive Steganographie und zeigen anhand von weit verbreiteten SSI-Varianten, dass unser Angriff funktioniert. Wir folgern, dass adaptive Steganographie spieltheoretisch beschrieben werden muss. Wir entwickeln ein spieltheoretisches Modell für solch ein System und berechnen die spieltheoretisch optimalen Strategien. Wir schlussfolgern, dass ein Steganograph diesen Strategien folgen sollte. Zudem entwickeln wir eine neue spieltheoretisch optimale Strategie zur Einbettung, die sogenannten Ausgleichseinbettungsstrategien.The  goal of digital steganography is to hide a secret communication in digital media. The common approach in steganography is to hide the secret messages in empirical cover objects. We are the first to define Steganographic Side Information (SSI). Our definition of SSI captures all relevant properties of SSI. We explain the common usage of SSI. All recent steganographic schemes use SSI to identify suitable areas fot the embedding change. We develop a targeted attack on four widely used variants of SSI, and show that our attack detects them almost perfectly. We argue that the steganographic competition must be framed with means of game theory. We present a game-theoretical framework that captures all relevant properties of such a steganographic system. We instantiate the framework with five different models and solve each of these models for game-theoretically optimal strategies. Inspired by our solutions, we give a new paradigm for secure adaptive steganography, the so-called equalizer embedding strategies

Detection and Mitigation of Steganographic Malware

A new attack trend concerns the use of some form of steganography and information hiding to make malware stealthier and able to elude many standard security mechanisms. Therefore, this Thesis addresses the detection and the mitigation of this class of threats. In particular, it considers malware implementing covert communications within network traffic or cloaking malicious payloads within digital images. The first research contribution of this Thesis is in the detection of network covert channels. Unfortunately, the literature on the topic lacks of real traffic traces or attack samples to perform precise tests or security assessments. Thus, a propaedeutic research activity has been devoted to develop two ad-hoc tools. The first allows to create covert channels targeting the IPv6 protocol by eavesdropping flows, whereas the second allows to embed secret data within arbitrary traffic traces that can be replayed to perform investigations in realistic conditions. This Thesis then starts with a security assessment concerning the impact of hidden network communications in production-quality scenarios. Results have been obtained by considering channels cloaking data in the most popular protocols (e.g., TLS, IPv4/v6, and ICMPv4/v6) and showcased that de-facto standard intrusion detection systems and firewalls (i.e., Snort, Suricata, and Zeek) are unable to spot this class of hazards. Since malware can conceal information (e.g., commands and configuration files) in almost every protocol, traffic feature or network element, configuring or adapting pre-existent security solutions could be not straightforward. Moreover, inspecting multiple protocols, fields or conversations at the same time could lead to performance issues. Thus, a major effort has been devoted to develop a suite based on the extended Berkeley Packet Filter (eBPF) to gain visibility over different network protocols/components and to efficiently collect various performance indicators or statistics by using a unique technology. This part of research allowed to spot the presence of network covert channels targeting the header of the IPv6 protocol or the inter-packet time of generic network conversations. In addition, the approach based on eBPF turned out to be very flexible and also allowed to reveal hidden data transfers between two processes co-located within the same host. Another important contribution of this part of the Thesis concerns the deployment of the suite in realistic scenarios and its comparison with other similar tools. Specifically, a thorough performance evaluation demonstrated that eBPF can be used to inspect traffic and reveal the presence of covert communications also when in the presence of high loads, e.g., it can sustain rates up to 3 Gbit/s with commodity hardware. To further address the problem of revealing network covert channels in realistic environments, this Thesis also investigates malware targeting traffic generated by Internet of Things devices. In this case, an incremental ensemble of autoencoders has been considered to face the ''unknown'' location of the hidden data generated by a threat covertly exchanging commands towards a remote attacker. The second research contribution of this Thesis is in the detection of malicious payloads hidden within digital images. In fact, the majority of real-world malware exploits hiding methods based on Least Significant Bit steganography and some of its variants, such as the Invoke-PSImage mechanism. Therefore, a relevant amount of research has been done to detect the presence of hidden data and classify the payload (e.g., malicious PowerShell scripts or PHP fragments). To this aim, mechanisms leveraging Deep Neural Networks (DNNs) proved to be flexible and effective since they can learn by combining raw low-level data and can be updated or retrained to consider unseen payloads or images with different features. To take into account realistic threat models, this Thesis studies malware targeting different types of images (i.e., favicons and icons) and various payloads (e.g., URLs and Ethereum addresses, as well as webshells). Obtained results showcased that DNNs can be considered a valid tool for spotting the presence of hidden contents since their detection accuracy is always above 90% also when facing ''elusion'' mechanisms such as basic obfuscation techniques or alternative encoding schemes. Lastly, when detection or classification are not possible (e.g., due to resource constraints), approaches enforcing ''sanitization'' can be applied. Thus, this Thesis also considers autoencoders able to disrupt hidden malicious contents without degrading the quality of the image

Big Data Security (Volume 3)

After a short description of the key concepts of big data the book explores on the secrecy and security threats posed especially by cloud based data storage. It delivers conceptual frameworks and models along with case studies of recent technology

System Steganalysis: Implementation Vulnerabilities and Side-Channel Attacks Against Digital Steganography Systems

Steganography is the process of hiding information in plain sight, it is a technology that can be used to hide data and facilitate secret communications. Steganography is commonly seen in the digital domain where the pervasive nature of media content (image, audio, video) provides an ideal avenue for hiding secret information. In recent years, video steganography has shown to be a highly suitable alternative to image and audio steganography due to its potential advantages (capacity, flexibility, popularity). An increased interest towards research in video steganography has led to the development of video stego-systems that are now available to the public. Many of these stego-systems have not yet been subjected to analysis or evaluation, and their capabilities for performing secure, practical, and effective video steganography are unknown. This thesis presents a comprehensive analysis of the state-of-the-art in practical video steganography. Video-based stego-systems are identified and examined using steganalytic techniques (system steganalysis) to determine the security practices of relevant stego-systems. The research in this thesis is conducted through a series of case studies that aim to provide novel insights in the field of steganalysis and its capabilities towards practical video steganography. The results of this work demonstrate the impact of system attacks over the practical state-of-the-art in video steganography. Through this research, it is evident that video-based stego-systems are highly vulnerable and fail to follow many of the well-understood security practices in the field. Consequently, it is possible to confidently detect each stego-system with a high rate of accuracy. As a result of this research, it is clear that current work in practical video steganography demonstrates a failure to address key principles and best practices in the field. Continued efforts to address this will provide safe and secure steganographic technologies