504 research outputs found

    Digital image forensics via meta-learning and few-shot learning

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    Digital images are a substantial portion of the information conveyed by social media, the Internet, and television in our daily life. In recent years, digital images have become not only one of the public information carriers, but also a crucial piece of evidence. The widespread availability of low-cost, user-friendly, and potent image editing software and mobile phone applications facilitates altering images without professional expertise. Consequently, safeguarding the originality and integrity of digital images has become a difficulty. Forgers commonly use digital image manipulation to transmit misleading information. Digital image forensics investigates the irregular patterns that might result from image alteration. It is crucial to information security. Over the past several years, machine learning techniques have been effectively used to identify image forgeries. Convolutional Neural Networks(CNN) are a frequent machine learning approach. A standard CNN model could distinguish between original and manipulated images. In this dissertation, two CNN models are introduced to recognize seam carving and Gaussian filtering. Training a conventional CNN model for a new similar image forgery detection task, one must start from scratch. Additionally, many types of tampered image data are challenging to acquire or simulate. Meta-learning is an alternative learning paradigm in which a machine learning model gets experience across numerous related tasks and uses this expertise to improve its future learning performance. Few-shot learning is a method for acquiring knowledge from few data. It can classify images with as few as one or two examples per class. Inspired by meta-learning and few-shot learning, this dissertation proposed a prototypical networks model capable of resolving a collection of related image forgery detection problems. Unlike traditional CNN models, the proposed prototypical networks model does not need to be trained from scratch for a new task. Additionally, it drastically decreases the quantity of training images

    Forensic research on detecting seam carving in digital images

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    Digital images have been playing an important role in our daily life for the last several decades. Naturally, image editing technologies have been tremendously developed due to the increasing demands. As a result, digital images can be easily manipulated on a personal computer or even a cellphone for many purposes nowadays, so that the authenticity of digital images becomes an important issue. In this dissertation research, four machine learning based forensic methods are presented to detect one of the popular image editing techniques, called ‘seam carving’. To reveal seam carving applied to uncompressed images from the perspective of energy distribution change, an energy based statistical model is proposed as the first work in this dissertation. Features measured global energy of images, remaining optimal seams, and noise level are extracted from four local derivative pattern (LDP) domains instead of from the original pixel domain to heighten the energy change caused by seam carving. A support vector machine (SVM) based classifier is employed to determine whether an image has been seam carved or not. In the second work, an advanced feature model is presented for seam carving detection by investigating the statistical variation among neighboring pixels. Comprised with three types of statistical features, i.e., LDP features, Markov features, and SPAM features, the powerful feature model significantly improved the state-of-the-art accuracy in detecting low carving rate seam carving. After the feature selection by utilizing SVM based recursive feature elimination (SVM-RFE), with a small amount of features selected from the proposed model the overall performance is further improved. Combining above mentioned two works, a hybrid feature model is then proposed as the third work to further boost the accuracy in detecting seam carving at low carving rate. The proposed model consists of two sets of features, which capture energy change and neighboring relationship variation respectively, achieves remarkable performance on revealing seam carving, especially low carving rate seam carving, in digital images. Besides these three hand crafted feature models, a deep convolutional neural network is designed for seam carving detection. It is the first work that successfully utilizes deep learning technology to solve this forensic problem. The experimental works demonstrate their much more improved performance in the cases where the amount of seam carving is not serious. Although these four pieces of work move the seam carving detection ahead substantially, future research works with more advanced statistical model or deep neural network along this line are expected

    Conditional Entrench Spatial Domain Steganography

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    Steganography is a technique of concealing the secret information in a digital carrier media, so that only the authorized recipient can detect the presence of secret information. In this paper, we propose a spatial domain steganography method for embedding secret information on conditional basis using 1-Bit of Most Significant Bit (MSB). The cover image is decomposed into blocks of 8*8 matrix size. The first block of cover image is embedded with 8 bits of upper bound and lower bound values required for retrieving payload at the destination. The mean of median values and difference between consecutive pixels of each 8*8 block of cover image is determined to embed payload in 3 bits of Least Significant Bit (LSB) and 1 bit of MSB based on prefixed conditions. It is observed that the capacity and security is improved compared to the existing methods with reasonable PSNR

    Preserving Trustworthiness and Confidentiality for Online Multimedia

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    Technology advancements in areas of mobile computing, social networks, and cloud computing have rapidly changed the way we communicate and interact. The wide adoption of media-oriented mobile devices such as smartphones and tablets enables people to capture information in various media formats, and offers them a rich platform for media consumption. The proliferation of online services and social networks makes it possible to store personal multimedia collection online and share them with family and friends anytime anywhere. Considering the increasing impact of digital multimedia and the trend of cloud computing, this dissertation explores the problem of how to evaluate trustworthiness and preserve confidentiality of online multimedia data. The dissertation consists of two parts. The first part examines the problem of evaluating trustworthiness of multimedia data distributed online. Given the digital nature of multimedia data, editing and tampering of the multimedia content becomes very easy. Therefore, it is important to analyze and reveal the processing history of a multimedia document in order to evaluate its trustworthiness. We propose a new forensic technique called ``Forensic Hash", which draws synergy between two related research areas of image hashing and non-reference multimedia forensics. A forensic hash is a compact signature capturing important information from the original multimedia document to assist forensic analysis and reveal processing history of a multimedia document under question. Our proposed technique is shown to have the advantage of being compact and offering efficient and accurate analysis to forensic questions that cannot be easily answered by convention forensic techniques. The answers that we obtain from the forensic hash provide valuable information on the trustworthiness of online multimedia data. The second part of this dissertation addresses the confidentiality issue of multimedia data stored with online services. The emerging cloud computing paradigm makes it attractive to store private multimedia data online for easy access and sharing. However, the potential of cloud services cannot be fully reached unless the issue of how to preserve confidentiality of sensitive data stored in the cloud is addressed. In this dissertation, we explore techniques that enable confidentiality-preserving search of encrypted multimedia, which can play a critical role in secure online multimedia services. Techniques from image processing, information retrieval, and cryptography are jointly and strategically applied to allow efficient rank-ordered search over encrypted multimedia database and at the same time preserve data confidentiality against malicious intruders and service providers. We demonstrate high efficiency and accuracy of the proposed techniques and provide a quantitative comparative study with conventional techniques based on heavy-weight cryptography primitives

    Data Hiding and Its Applications

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    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

    Adaptation of Images and Videos for Different Screen Sizes

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    With the increasing popularity of smartphones and similar mobile devices, the demand for media to consume on the go rises. As most images and videos today are captured with HD or even higher resolutions, there is a need to adapt them in a content-aware fashion before they can be watched comfortably on screens with small sizes and varying aspect ratios. This process is called retargeting. Most distortions during this process are caused by a change of the aspect ratio. Thus, retargeting mainly focuses on adapting the aspect ratio of a video while the rest can be scaled uniformly. The main objective of this dissertation is to contribute to the modern image and video retargeting, especially regarding the potential of the seam carving operator. There are still unsolved problems in this research field that should be addressed in order to improve the quality of the results or speed up the performance of the retargeting process. This dissertation presents novel algorithms that are able to retarget images, videos and stereoscopic videos while dealing with problems like the preservation of straight lines or the reduction of the required memory space and computation time. Additionally, a GPU implementation is used to achieve the retargeting of videos in real-time. Furthermore, an enhancement of face detection is presented which is able to distinguish between faces that are important for the retargeting and faces that are not. Results show that the developed techniques are suitable for the desired scenarios

    Task-based Adaptation of Graphical Content in Smart Visual Interfaces

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    To be effective visual representations must be adapted to their respective context of use, especially in so-called Smart Visual Interfaces striving to present specifically those information required for the task at hand. This thesis proposes a generic approach that facilitate the automatic generation of task-specific visual representations from suitable task descriptions. It is discussed how the approach is applied to four principal content types raster images, 2D vector and 3D graphics as well as data visualizations, and how existing display techniques can be integrated into the approach.Effektive visuelle Repräsentationen müssen an den jeweiligen Nutzungskontext angepasst sein, insbesondere in sog. Smart Visual Interfaces, welche anstreben, möglichst genau für die aktuelle Aufgabe benötigte Informationen anzubieten. Diese Arbeit entwirft einen generischen Ansatz zur automatischen Erzeugung aufgabenspezifischer Darstellungen anhand geeigneter Aufgabenbeschreibungen. Es wird gezeigt, wie dieser Ansatz auf vier grundlegende Inhaltstypen Rasterbilder, 2D-Vektor- und 3D-Grafik sowie Datenvisualisierungen anwendbar ist, und wie existierende Darstellungstechniken integrierbar sind

    Photo response non-uniformity based image forensics in the presence of challenging factors

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    With the ever-increasing prevalence of digital imaging devices and the rapid development of networks, the sharing of digital images becomes ubiquitous in our daily life. However, the pervasiveness of powerful image-editing tools also makes the digital images an easy target for malicious manipulations. Thus, to prevent people from falling victims to fake information and trace the criminal activities, digital image forensics methods like source camera identification, source oriented image clustering and image forgery detections have been developed. Photo response non-uniformity (PRNU), which is an intrinsic sensor noise arises due to the pixels non-uniform response to the incident, has been used as a powerful tool for image device fingerprinting. The forensic community has developed a vast number of PRNU-based methods in different fields of digital image forensics. However, with the technology advancement in digital photography, the emergence of photo-sharing social networking sites, as well as the anti-forensics attacks targeting the PRNU, it brings new challenges to PRNU-based image forensics. For example, the performance of the existing forensic methods may deteriorate due to different camera exposure parameter settings and the efficacy of the PRNU-based methods can be directly challenged by image editing tools from social network sites or anti-forensics attacks. The objective of this thesis is to investigate and design effective methods to mitigate some of these challenges on PRNU-based image forensics. We found that the camera exposure parameter settings, especially the camera sensitivity, which is commonly known by the name of the ISO speed, can influence the PRNU-based image forgery detection. Hence, we first construct the Warwick Image Forensics Dataset, which contains images taken with diverse exposure parameter settings to facilitate further studies. To address the impact from ISO speed on PRNU-based image forgery detection, an ISO speed-specific correlation prediction process is proposed with a content-based ISO speed inference method to facilitate the process even if the ISO speed information is not available. We also propose a three-step framework to allow the PRNUbased source oriented clustering methods to perform successfully on Instagram images, despite some built-in image filters from Instagram may significantly distort PRNU. Additionally, for the binary classification of detecting whether an image's PRNU is attacked or not, we propose a generative adversarial network-based training strategy for a neural network-based classifier, which makes the classifier generalize better for images subject to unprecedented attacks. The proposed methods are evaluated on public benchmarking datasets and our Warwick Image Forensics Dataset, which is released to the public as well. The experimental results validate the effectiveness of the methods proposed in this thesis
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