Deep Convolutional Neural Network to Detect J-UNIWARD

This paper presents an empirical study on applying convolutional neural networks (CNNs) to detecting J-UNIWARD, one of the most secure JPEG steganographic method. Experiments guiding the architectural design of the CNNs have been conducted on the JPEG compressed BOSSBase containing 10,000 covers of size 512x512. Results have verified that both the pooling method and the depth of the CNNs are critical for performance. Results have also proved that a 20-layer CNN, in general, outperforms the most sophisticated feature-based methods, but its advantage gradually diminishes on hard-to-detect cases. To show that the performance generalizes to large-scale databases and to different cover sizes, one experiment has been conducted on the CLS-LOC dataset of ImageNet containing more than one million covers cropped to unified size of 256x256. The proposed 20-layer CNN has cut the error achieved by a CNN recently proposed for large-scale JPEG steganalysis by 35%. Source code is available via GitHub: https://github.com/GuanshuoXu/deep_cnn_jpeg_steganalysisComment: Accepted by IH&MMSec 2017. This is a personal cop

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

Data Hiding and Its Applications

Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others

Source identification in image forensics

Source identification is one of the most important tasks in digital image forensics. In fact, the ability to reliably associate an image with its acquisition device may be crucial both during investigations and before a court of law. For example, one may be interested in proving that a certain photo was taken by his/her camera, in order to claim intellectual property. On the contrary, it may be law enforcement agencies that are interested to trace back the origin of some images, because they violate the law themselves (e.g. do not respect privacy laws), or maybe they point to subjects involved in unlawful and dangerous activities (like terrorism, pedo-pornography, etc). More in general, proving, beyond reasonable doubts, that a photo was taken by a given camera, may be an important element for decisions in court. The key assumption of forensic source identification is that acquisition devices leave traces in the acquired content, and that instances of these traces are specific to the respective (class of) device(s). This kind of traces is present in the so-called device fingerprint. The name stems from the forensic value of human fingerprints. Motivated by the importance of the source identification in digital image forensics community and the need of reliable techniques using device fingerprint, the work developed in the Ph.D. thesis concerns different source identification level, using both feature-based and PRNU-based approach for model and device identification. In addition, it is also shown that counter-forensics methods can easily attack machine learning techniques for image forgery detection. In model identification, an analysis of hand-crafted local features and deep learning ones has been considered for the basic two-class classification problem. In addition, a comparison with the limited knowledge and the blind scenario are presented. Finally, an application of camera model identification on various iris sensor models is conducted. A blind scenario technique that faces the problem of device source identification using the PRNU-based approach is also proposed. With the use of the correlation between single-image sensor noise, a blind two-step source clustering is proposed. In the first step correlation clustering together with ensemble method is used to obtain an initial partition, which is then refined in the second step by means of a Bayesian approach. Experimental results show that this proposal outperforms the state-of-the-art techniques and still give an acceptable performance when considering images downloaded from Facebook

DEEP LEARNING FOR FORENSICS

The advent of media sharing platforms and the easy availability of advanced photo or video editing software have resulted in a large quantity of manipulated images and videos being shared on the internet. While the intent behind such manipulations varies widely, concerns on the spread of fake news and misinformation is growing. Therefore, detecting manipulation has become an emerging necessity. Different from traditional classification, semantic object detection or segmentation, manipulation detection/classification pays more attention to low-level tampering artifacts than to semantic content. The main challenges in this problem include (a) investigating features to reveal tampering artifacts, (b) developing generic models which are robust to a large scale of post-processing methods, (c) applying algorithms to higher resolution in real scenarios and (d) handling the new emerging manipulation techniques. In this dissertation, we propose approaches to tackling these challenges. Manipulation detection utilizes both low-level tamper artifacts and semantic contents, suggesting that richer features needed to be harnessed to reveal more evidence. To learn rich features, we propose a two-stream Faster R-CNN network and train it end-to-end to detect the tampered regions given a manipulated image. Experiments on four standard image manipulation datasets demonstrate that our two-stream framework outperforms each individual stream, and also achieves state-of-the-art performance compared to alternative methods with robustness to resizing and compression. Additionally, to extend manipulation detection from image to video, we introduce VIDNet, Video Inpainting Detection Network, which contains an encoder-decoder architecture with a quad-directional local attention module. To reveal artifacts encoded in compression, VIDNet additionally takes in Error Level Analysis (ELA) frames to augment RGB frames, producing multimodal features at different levels with an encoder. Besides, to improve the generalization of manipulation detection model, we introduce a manipulated image generation process that creates true positives using currently available datasets. Drawing from traditional work on image blending, we propose a novel generator for creating such examples. In addition, we also propose to further create examples that force the algorithm to focus on boundary artifacts during training. Extensive experimental results validate our proposal. Furthermore, to apply deep learning models to high resolution scenarios efficiently, we treat the problem as a mask refinement given a coarse low resolution prediction. We propose to convert the regions of interest into strip images and compute a boundary prediction in the strip domain. Extensive experiments on both the public and a newly created high resolution dataset strongly validate our approach. Finally, to handle new emerging manipulation techniques while preserving performance on learned manipulation, we investigate incremental learning. We propose a multi-model and multi-level knowledge distillation strategy to preserve performance on old categories while training on new categories. Experiments on standard incremental learning benchmarks show that our method improves the overall performance over standard distillation techniques

Steganalytic Methods for 3D Objects

This PhD thesis provides new research results in the area of using 3D features for steganalysis. The research study presented in the thesis proposes new sets of 3D features, greatly extending the previously proposed features. The proposed steganlytic feature set includes features representing the vertex normal, curvature ratio, Gaussian curvature, the edge and vertex position of the 3D objects in the spherical coordinate system. Through a second contribution, this thesis presents a 3D wavelet multiresolution analysis-based steganalytic method. The proposed method extracts the 3D steganalytic features from meshes of different resolutions. The third contribution proposes a robustness and relevance-based feature selection method for solving the cover-source mismatch problem in 3D steganalysis. This method selects those 3D features that are robust to the variation of the cover source, while preserving the relevance of such features to the class label. All the proposed methods are applied for identifying stego-meshes produced by several steganographic algorithms

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

Design of a secure architecture for the exchange of biomedical information in m-Health scenarios

El paradigma de m-Salud (salud móvil) aboga por la integración masiva de las más avanzadas tecnologías de comunicación, red móvil y sensores en aplicaciones y sistemas de salud, para fomentar el despliegue de un nuevo modelo de atención clínica centrada en el usuario/paciente. Este modelo tiene por objetivos el empoderamiento de los usuarios en la gestión de su propia salud (p.ej. aumentando sus conocimientos, promocionando estilos de vida saludable y previniendo enfermedades), la prestación de una mejor tele-asistencia sanitaria en el hogar para ancianos y pacientes crónicos y una notable disminución del gasto de los Sistemas de Salud gracias a la reducción del número y la duración de las hospitalizaciones. No obstante, estas ventajas, atribuidas a las aplicaciones de m-Salud, suelen venir acompañadas del requisito de un alto grado de disponibilidad de la información biomédica de sus usuarios para garantizar una alta calidad de servicio, p.ej. fusionar varias señales de un usuario para obtener un diagnóstico más preciso. La consecuencia negativa de cumplir esta demanda es el aumento directo de las superficies potencialmente vulnerables a ataques, lo que sitúa a la seguridad (y a la privacidad) del modelo de m-Salud como factor crítico para su éxito. Como requisito no funcional de las aplicaciones de m-Salud, la seguridad ha recibido menos atención que otros requisitos técnicos que eran más urgentes en etapas de desarrollo previas, tales como la robustez, la eficiencia, la interoperabilidad o la usabilidad. Otro factor importante que ha contribuido a retrasar la implementación de políticas de seguridad sólidas es que garantizar un determinado nivel de seguridad implica unos costes que pueden ser muy relevantes en varias dimensiones, en especial en la económica (p.ej. sobrecostes por la inclusión de hardware extra para la autenticación de usuarios), en el rendimiento (p.ej. reducción de la eficiencia y de la interoperabilidad debido a la integración de elementos de seguridad) y en la usabilidad (p.ej. configuración más complicada de dispositivos y aplicaciones de salud debido a las nuevas opciones de seguridad). Por tanto, las soluciones de seguridad que persigan satisfacer a todos los actores del contexto de m-Salud (usuarios, pacientes, personal médico, personal técnico, legisladores, fabricantes de dispositivos y equipos, etc.) deben ser robustas y al mismo tiempo minimizar sus costes asociados. Esta Tesis detalla una propuesta de seguridad, compuesta por cuatro grandes bloques interconectados, para dotar de seguridad a las arquitecturas de m-Salud con unos costes reducidos. El primer bloque define un esquema global que proporciona unos niveles de seguridad e interoperabilidad acordes con las características de las distintas aplicaciones de m-Salud. Este esquema está compuesto por tres capas diferenciadas, diseñadas a la medidas de los dominios de m-Salud y de sus restricciones, incluyendo medidas de seguridad adecuadas para la defensa contra las amenazas asociadas a sus aplicaciones de m-Salud. El segundo bloque establece la extensión de seguridad de aquellos protocolos estándar que permiten la adquisición, el intercambio y/o la administración de información biomédica -- por tanto, usados por muchas aplicaciones de m-Salud -- pero no reúnen los niveles de seguridad detallados en el esquema previo. Estas extensiones se concretan para los estándares biomédicos ISO/IEEE 11073 PHD y SCP-ECG. El tercer bloque propone nuevas formas de fortalecer la seguridad de los tests biomédicos, que constituyen el elemento esencial de muchas aplicaciones de m-Salud de carácter clínico, mediante codificaciones novedosas. Finalmente el cuarto bloque, que se sitúa en paralelo a los anteriores, selecciona herramientas genéricas de seguridad (elementos de autenticación y criptográficos) cuya integración en los otros bloques resulta idónea, y desarrolla nuevas herramientas de seguridad, basadas en señal -- embedding y keytagging --, para reforzar la protección de los test biomédicos.The paradigm of m-Health (mobile health) advocates for the massive integration of advanced mobile communications, network and sensor technologies in healthcare applications and systems to foster the deployment of a new, user/patient-centered healthcare model enabling the empowerment of users in the management of their health (e.g. by increasing their health literacy, promoting healthy lifestyles and the prevention of diseases), a better home-based healthcare delivery for elderly and chronic patients and important savings for healthcare systems due to the reduction of hospitalizations in number and duration. It is a fact that many m-Health applications demand high availability of biomedical information from their users (for further accurate analysis, e.g. by fusion of various signals) to guarantee high quality of service, which on the other hand entails increasing the potential surfaces for attacks. Therefore, it is not surprising that security (and privacy) is commonly included among the most important barriers for the success of m-Health. As a non-functional requirement for m-Health applications, security has received less attention than other technical issues that were more pressing at earlier development stages, such as reliability, eficiency, interoperability or usability. Another fact that has contributed to delaying the enforcement of robust security policies is that guaranteeing a certain security level implies costs that can be very relevant and that span along diferent dimensions. These include budgeting (e.g. the demand of extra hardware for user authentication), performance (e.g. lower eficiency and interoperability due to the addition of security elements) and usability (e.g. cumbersome configuration of devices and applications due to security options). Therefore, security solutions that aim to satisfy all the stakeholders in the m-Health context (users/patients, medical staff, technical staff, systems and devices manufacturers, regulators, etc.) shall be robust and, at the same time, minimize their associated costs. This Thesis details a proposal, composed of four interrelated blocks, to integrate appropriate levels of security in m-Health architectures in a cost-efcient manner. The first block designes a global scheme that provides different security and interoperability levels accordingto how critical are the m-Health applications to be implemented. This consists ofthree layers tailored to the m-Health domains and their constraints, whose security countermeasures defend against the threats of their associated m-Health applications. Next, the second block addresses the security extension of those standard protocols that enable the acquisition, exchange and/or management of biomedical information | thus, used by many m-Health applications | but do not meet the security levels described in the former scheme. These extensions are materialized for the biomedical standards ISO/IEEE 11073 PHD and SCP-ECG. Then, the third block proposes new ways of enhancing the security of biomedical standards, which are the centerpiece of many clinical m-Health applications, by means of novel codings. Finally the fourth block, with is parallel to the others, selects generic security methods (for user authentication and cryptographic protection) whose integration in the other blocks results optimal, and also develops novel signal-based methods (embedding and keytagging) for strengthening the security of biomedical tests. The layer-based extensions of the standards ISO/IEEE 11073 PHD and SCP-ECG can be considered as robust, cost-eficient and respectful with their original features and contents. The former adds no attributes to its data information model, four new frames to the service model |and extends four with new sub-frames|, and only one new sub-state to the communication model. Furthermore, a lightweight architecture consisting of a personal health device mounting a 9 MHz processor and an aggregator mounting a 1 GHz processor is enough to transmit a 3-lead electrocardiogram in real-time implementing the top security layer. The extra requirements associated to this extension are an initial configuration of the health device and the aggregator, tokens for identification/authentication of users if these devices are to be shared and the implementation of certain IHE profiles in the aggregator to enable the integration of measurements in healthcare systems. As regards to the extension of SCP-ECG, it only adds a new section with selected security elements and syntax in order to protect the rest of file contents and provide proper role-based access control. The overhead introduced in the protected SCP-ECG is typically 2{13 % of the regular file size, and the extra delays to protect a newly generated SCP-ECG file and to access it for interpretation are respectively a 2{10 % and a 5 % of the regular delays. As regards to the signal-based security techniques developed, the embedding method is the basis for the proposal of a generic coding for tests composed of biomedical signals, periodic measurements and contextual information. This has been adjusted and evaluated with electrocardiogram and electroencephalogram-based tests, proving the objective clinical quality of the coded tests, the capacity of the coding-access system to operate in real-time (overall delays of 2 s for electrocardiograms and 3.3 s for electroencephalograms) and its high usability. Despite of the embedding of security and metadata to enable m-Health services, the compression ratios obtained by this coding range from ' 3 in real-time transmission to ' 5 in offline operation. Complementarily, keytagging permits associating information to images (and other signals) by means of keys in a secure and non-distorting fashion, which has been availed to implement security measures such as image authentication, integrity control and location of tampered areas, private captioning with role-based access control, traceability and copyright protection. The tests conducted indicate a remarkable robustness-capacity tradeoff that permits implementing all this measures simultaneously, and the compatibility of keytagging with JPEG2000 compression, maintaining this tradeoff while setting the overall keytagging delay in only ' 120 ms for any image size | evidencing the scalability of this technique. As a general conclusion, it has been demonstrated and illustrated with examples that there are various, complementary and structured manners to contribute in the implementation of suitable security levels for m-Health architectures with a moderate cost in budget, performance, interoperability and usability. The m-Health landscape is evolving permanently along all their dimensions, and this Thesis aims to do so with its security. Furthermore, the lessons learned herein may offer further guidance for the elaboration of more comprehensive and updated security schemes, for the extension of other biomedical standards featuring low emphasis on security or privacy, and for the improvement of the state of the art regarding signal-based protection methods and applications