Aligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks

Due to the wide diffusion of JPEG coding standard, the image forensic community has devoted significant attention to the development of double JPEG (DJPEG) compression detectors through the years. The ability of detecting whether an image has been compressed twice provides paramount information toward image authenticity assessment. Given the trend recently gained by convolutional neural networks (CNN) in many computer vision tasks, in this paper we propose to use CNNs for aligned and non-aligned double JPEG compression detection. In particular, we explore the capability of CNNs to capture DJPEG artifacts directly from images. Results show that the proposed CNN-based detectors achieve good performance even with small size images (i.e., 64x64), outperforming state-of-the-art solutions, especially in the non-aligned case. Besides, good results are also achieved in the commonly-recognized challenging case in which the first quality factor is larger than the second one.Comment: Submitted to Journal of Visual Communication and Image Representation (first submission: March 20, 2017; second submission: August 2, 2017

Smart techniques and tools to detect Steganography - a viable practice to Security Office Department

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementInternet is today a commodity and a way for being connect to the world. It is through Internet is where most of the information is shared and where people run their businesses. However, there are some people that make a malicious use of it. Cyberattacks have been increasing all over the recent years, targeting people and organizations, looking to perform illegal actions. Cyber criminals are always looking for new ways to deliver malware to victims to launch an attack. Millions of users share images and photos on their social networks and generally users find them safe to use. Contrary to what most people think, images can contain a malicious payload and perform harmful actions. Steganography is the technique of hiding data, which, combined with media files, can be used to place malicious code. This problem, leveraged by the continuous media file sharing through massive use of digital platforms, may become a worldwide threat in malicious content sharing. Like phishing, people and organizations must be trained to suspect about inappropriate content and implement the proper set of actions to reduce probability of infections when accessing files supposed to be inoffensive. The aim of this study will try to help people and organizations by trying to set a toolbox where it can be possible to get some tools and techniques to assist in dealing with this kind of situations. A theoretical overview will be performed over other concepts such as Steganalysis, touching also Deep Learning and in Machine Learning to assess which is the range of its applicability in find solutions in detection and facing these situations. In addition, understanding the current main technologies, architectures and users’ hurdles will play an important role in designing and developing the proposed toolbox artifact

Persistent Homology Tools for Image Analysis

Topological Data Analysis (TDA) is a new field of mathematics emerged rapidly since the first decade of the century from various works of algebraic topology and geometry. The goal of TDA and its main tool of persistent homology (PH) is to provide topological insight into complex and high dimensional datasets. We take this premise onboard to get more topological insight from digital image analysis and quantify tiny low-level distortion that are undetectable except possibly by highly trained persons. Such image distortion could be caused intentionally (e.g. by morphing and steganography) or naturally in abnormal human tissue/organ scan images as a result of onset of cancer or other diseases. The main objective of this thesis is to design new image analysis tools based on persistent homological invariants representing simplicial complexes on sets of pixel landmarks over a sequence of distance resolutions. We first start by proposing innovative automatic techniques to select image pixel landmarks to build a variety of simplicial topologies from a single image. Effectiveness of each image landmark selection demonstrated by testing on different image tampering problems such as morphed face detection, steganalysis and breast tumour detection. Vietoris-Rips simplicial complexes constructed based on the image landmarks at an increasing distance threshold and topological (homological) features computed at each threshold and summarized in a form known as persistent barcodes. We vectorise the space of persistent barcodes using a technique known as persistent binning where we demonstrated the strength of it for various image analysis purposes. Different machine learning approaches are adopted to develop automatic detection of tiny texture distortion in many image analysis applications. Homological invariants used in this thesis are the 0 and 1 dimensional Betti numbers. We developed an innovative approach to design persistent homology (PH) based algorithms for automatic detection of the above described types of image distortion. In particular, we developed the first PH-detector of morphing attacks on passport face biometric images. We shall demonstrate significant accuracy of 2 such morph detection algorithms with 4 types of automatically extracted image landmarks: Local Binary patterns (LBP), 8-neighbour super-pixels (8NSP), Radial-LBP (R-LBP) and centre-symmetric LBP (CS-LBP). Using any of these techniques yields several persistent barcodes that summarise persistent topological features that help gaining insights into complex hidden structures not amenable by other image analysis methods. We shall also demonstrate significant success of a similarly developed PH-based universal steganalysis tool capable for the detection of secret messages hidden inside digital images. We also argue through a pilot study that building PH records from digital images can differentiate breast malignant tumours from benign tumours using digital mammographic images. The research presented in this thesis creates new opportunities to build real applications based on TDA and demonstrate many research challenges in a variety of image processing/analysis tasks. For example, we describe a TDA-based exemplar image inpainting technique (TEBI), superior to existing exemplar algorithm, for the reconstruction of missing image regions

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

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

Fuzzy Logic

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems

Information similarity metrics in information security and forensics

We study two information similarity measures, relative entropy and the similarity metric, and methods for estimating them. Relative entropy can be readily estimated with existing algorithms based on compression. The similarity metric, based on algorithmic complexity, proves to be more difficult to estimate due to the fact that algorithmic complexity itself is not computable. We again turn to compression for estimating the similarity metric. Previous studies rely on the compression ratio as an indicator for choosing compressors to estimate the similarity metric. This assumption, however, is fundamentally flawed. We propose a new method to benchmark compressors for estimating the similarity metric. To demonstrate its use, we propose to quantify the security of a stegosystem using the similarity metric. Unlike other measures of steganographic security, the similarity metric is not only a true distance metric, but it is also universal in the sense that it is asymptotically minimal among all computable metrics between two objects. Therefore, it accounts for all similarities between two objects. In contrast, relative entropy, a widely accepted steganographic security definition, only takes into consideration the statistical similarity between two random variables. As an application, we present a general method for benchmarking stegosystems. The method is general in the sense that it is not restricted to any covertext medium and therefore, can be applied to a wide range of stegosystems. For demonstration, we analyze several image stegosystems using the newly proposed similarity metric as the security metric. The results show the true security limits of stegosystems regardless of the chosen security metric or the existence of steganalysis detectors. In other words, this makes it possible to show that a stegosystem with a large similarity metric is inherently insecure, even if it has not yet been broken