Digital Multimedia Forensics and Anti-Forensics

As the use of digital multimedia content such as images and video has increased, so has the means and the incentive to create digital forgeries. Presently, powerful editing software allows forgers to create perceptually convincing digital forgeries. Accordingly, there is a great need for techniques capable of authenticating digital multimedia content. In response to this, researchers have begun developing digital forensic techniques capable of identifying digital forgeries. These forensic techniques operate by detecting imperceptible traces left by editing operations in digital multimedia content. In this dissertation, we propose several new digital forensic techniques to detect evidence of editing in digital multimedia content. We begin by identifying the fingerprints left by pixel value mappings and show how these can be used to detect the use of contrast enhancement in images. We use these fingerprints to perform a number of additional forensic tasks such as identifying cut-and-paste forgeries, detecting the addition of noise to previously JPEG compressed images, and estimating the contrast enhancement mapping used to alter an image. Additionally, we consider the problem of multimedia security from the forger's point of view. We demonstrate that an intelligent forger can design anti-forensic operations to hide editing fingerprints and fool forensic techniques. We propose an anti-forensic technique to remove compression fingerprints from digital images and show that this technique can be used to fool several state-of-the-art forensic algorithms. We examine the problem of detecting frame deletion in digital video and develop both a technique to detect frame deletion and an anti-forensic technique to hide frame deletion fingerprints. We show that this anti-forensic operation leaves behind fingerprints of its own and propose a technique to detect the use of frame deletion anti-forensics. The ability of a forensic investigator to detect both editing and the use of anti-forensics results in a dynamic interplay between the forger and forensic investigator. We use develop a game theoretic framework to analyze this interplay and identify the set of actions that each party will rationally choose. Additionally, we show that anti-forensics can be used protect against reverse engineering. To demonstrate this, we propose an anti-forensic module that can be integrated into digital cameras to protect color interpolation methods

Active and passive approaches for image authentication

Ph.DDOCTOR OF PHILOSOPH

Video enhancement : content classification and model selection

The purpose of video enhancement is to improve the subjective picture quality. The field of video enhancement includes a broad category of research topics, such as removing noise in the video, highlighting some specified features and improving the appearance or visibility of the video content. The common difficulty in this field is how to make images or videos more beautiful, or subjectively better. Traditional approaches involve lots of iterations between subjective assessment experiments and redesigns of algorithm improvements, which are very time consuming. Researchers have attempted to design a video quality metric to replace the subjective assessment, but so far it is not successful. As a way to avoid heuristics in the enhancement algorithm design, least mean square methods have received considerable attention. They can optimize filter coefficients automatically by minimizing the difference between processed videos and desired versions through a training. However, these methods are only optimal on average but not locally. To solve the problem, one can apply the least mean square optimization for individual categories that are classified by local image content. The most interesting example is Kondo’s concept of local content adaptivity for image interpolation, which we found could be generalized into an ideal framework for content adaptive video processing. We identify two parts in the concept, content classification and adaptive processing. By exploring new classifiers for the content classification and new models for the adaptive processing, we have generalized a framework for more enhancement applications. For the part of content classification, new classifiers have been proposed to classify different image degradations such as coding artifacts and focal blur. For the coding artifact, a novel classifier has been proposed based on the combination of local structure and contrast, which does not require coding block grid detection. For the focal blur, we have proposed a novel local blur estimation method based on edges, which does not require edge orientation detection and shows more robust blur estimation. With these classifiers, the proposed framework has been extended to coding artifact robust enhancement and blur dependant enhancement. With the content adaptivity to more image features, the number of content classes can increase significantly. We show that it is possible to reduce the number of classes without sacrificing much performance. For the part of model selection, we have introduced several nonlinear filters to the proposed framework. We have also proposed a new type of nonlinear filter, trained bilateral filter, which combines both advantages of the original bilateral filter and the least mean square optimization. With these nonlinear filters, the proposed framework show better performance than with linear filters. Furthermore, we have shown a proof-of-concept for a trained approach to obtain contrast enhancement by a supervised learning. The transfer curves are optimized based on the classification of global or local image content. It showed that it is possible to obtain the desired effect by learning from other computationally expensive enhancement algorithms or expert-tuned examples through the trained approach. Looking back, the thesis reveals a single versatile framework for video enhancement applications. It widens the application scope by including new content classifiers and new processing models and offers scalabilities with solutions to reduce the number of classes, which can greatly accelerate the algorithm design

Motion compensated interpolation for subband coding of moving images

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (leaves 108-119).by Mark Daniel Polomski.M.S

Data compression techniques applied to high resolution high frame rate video technology

An investigation is presented of video data compression applied to microgravity space experiments using High Resolution High Frame Rate Video Technology (HHVT). An extensive survey of methods of video data compression, described in the open literature, was conducted. The survey examines compression methods employing digital computing. The results of the survey are presented. They include a description of each method and assessment of image degradation and video data parameters. An assessment is made of present and near term future technology for implementation of video data compression in high speed imaging system. Results of the assessment are discussed and summarized. The results of a study of a baseline HHVT video system, and approaches for implementation of video data compression, are presented. Case studies of three microgravity experiments are presented and specific compression techniques and implementations are recommended

Image statistical frameworks for digital image forensics

The advances of digital cameras, scanners, printers, image editing tools, smartphones, tablet personal computers as well as high-speed networks have made a digital image a conventional medium for visual information. Creation, duplication, distribution, or tampering of such a medium can be easily done, which calls for the necessity to be able to trace back the authenticity or history of the medium. Digital image forensics is an emerging research area that aims to resolve the imposed problem and has grown in popularity over the past decade. On the other hand, anti-forensics has emerged over the past few years as a relatively new branch of research, aiming at revealing the weakness of the forensic technology. These two sides of research move digital image forensic technologies to the next higher level. Three major contributions are presented in this dissertation as follows. First, an effective multi-resolution image statistical framework for digital image forensics of passive-blind nature is presented in the frequency domain. The image statistical framework is generated by applying Markovian rake transform to image luminance component. Markovian rake transform is the applications of Markov process to difference arrays which are derived from the quantized block discrete cosine transform 2-D arrays with multiple block sizes. The efficacy and universality of the framework is then evaluated in two major applications of digital image forensics: 1) digital image tampering detection; 2) classification of computer graphics and photographic images. Second, a simple yet effective anti-forensic scheme is proposed, capable of obfuscating double JPEG compression artifacts, which may vital information for image forensics, for instance, digital image tampering detection. Shrink-and-zoom (SAZ) attack, the proposed scheme, is simply based on image resizing and bilinear interpolation. The effectiveness of SAZ has been evaluated over two promising double JPEG compression schemes and the outcome reveals that the proposed scheme is effective, especially in the cases that the first quality factor is lower than the second quality factor. Third, an advanced textural image statistical framework in the spatial domain is proposed, utilizing local binary pattern (LBP) schemes to model local image statistics on various kinds of residual images including higher-order ones. The proposed framework can be implemented either in single- or multi-resolution setting depending on the nature of application of interest. The efficacy of the proposed framework is evaluated on two forensic applications: 1) steganalysis with emphasis on HUGO (Highly Undetectable Steganography), an advanced steganographic scheme embedding hidden data in a content-adaptive manner locally into some image regions which are difficult for modeling image statics; 2) image recapture detection (IRD). The outcomes of the evaluations suggest that the proposed framework is effective, not only for detecting local changes which is in line with the nature of HUGO, but also for detecting global difference (the nature of IRD)

Real Time Fpga Implementation Of A Training Based Content Adaptive Video Resolution Upconversion Algorithm

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Bilişim Enstitüsü, 2007Thesis (M.Sc.) -- İstanbul Technical University, Institute of Informatics, 2007Bu çalışmada, eğitim tabanlı, içerik uyarlamalı bir video çözünürlük yükseltme algoritması için, iş hattı ve kaynak paylaşımı kullanan yüksek performanslı bir donanım mimarisi önerilmiş ve önerilen yapı, 480x720 standart çözünürlükteki videonun 720x1280 yüksek çözünürlükte videoya dönüştürülmesi uygulaması için düşük maliyetli bir sahada programlanabilir kapı dizisi (SPKD (FPGA)) kullanarak gerçeklenmiştir. Donanım yapısı önerilen ve gerçeklenen, modifiye edilmiş çözünürlük sentezi algoritması (MÇS (MRS)), alt örnekleme işlemi sürecinde video sinyalinde kaybolan yüksek frekans bileşenlerinin, geniş bir video görüntü kümesi üzerinde gerçekleştirilen eğitim sürecinde elde edilen bilgi ile geri kazanılmasını hedefler. MÇS algoritması çıkış görüntüsünü oluşturan her piksel için 137 çarpma ve 120 toplama işlemi içerir. 480x720 standart çözünürlükte videonun 720x1280 yüksek çözünürlükte videoya dönüştürülmesi problemi, 27 Mhz giriş saat çevriminde üretilen piksel datası ile gerçek zaman kısıtları içerir. Hedeflenen FPGA için, tasarım, giriş piksel saat frekansının dört katı olan 108 Mhz saat frekansında çalışacak biçimde iş hattı yapısı kurulmuştur. Bu sayede çarpma ve toplama işlemleri için kaynak paylaşımı yapılmış ve, iş hattındaki saklayıcılarda ve kontrol lojiğinde küçük bir artış ile çarpıcı ve toplayıcı sayısı dörtte birine indirilmiştir. Önerilen yapının, saklayıcı transfer seviyesindeki tanımı, VHDL dili ile yazılmış, sabit noktalı C modeli ile VHDL modeli çıktıları karşılaştırılarak donanım yapısı doğrulanmıştır. Doğrulanan tasarım, Xilinx XC3S2000 FPGA kullanılarak gerçeklenmiş ve standart çözünürlükteki videonun yüksek çözünürlükte videoya dönüştürülmesi uygulaması için likit kristal ekranlı TV üzerinde test edilmiştir. Tasarım, FPGA içerisinde 3533 dilim ve yaklaşık 60 KB blok RAM yapısı kullanmaktadır. Tasarımın lojik kapı cinsinden karmaşıklığının, literatürdeki lineer video boyutlandırma algoritmaları ile yaklaşık aynı ölçekte olduğu görülmüştür.In this study, a high performance, pipelined, resource shared hardware architecture was proposed for a training based content adaptive video resolution up-conversion algorithm, and the proposed architecture was implemented in a field programmable gate array (FPGA), for a video standards conversion application where the input is standard definition (SD) video with 480x720 resolution, and the output is high definition (HD) video with 720x1280 resolution. Modified resolution synthesis (MRS), which was implemented in this study is a method, that aims to recover the missing spectrum at the down sampled image, by using information obtained by training with large set of images. MRS requires 137 multiplications and 120 additions per output pixel. For 480x720 to 720x1280 video conversion, the design is constrained by the input pixel rate which is 27 Mhz. For the targeted FPGA, the design was pipelined to work at 108 Mhz, four times the input pixel clock rate. Number of multipliers and adders were reduced by a factor of 4, with minor increase in the pipeline stages and the control logic complexity. Register transfer level (RTL) description of the proposed architecture was written in VHDL and RTL model was verified with fixed point C model outputs. The verified design was mapped to Xilinx XC3S2000 FPGA, and was tested on TV for SD to HD video conversion. The design uses 3533 slices, and 60KByte of block RAMS available in the FPGA. The logic gate count of the design is in the order of gate counts for bicubic scalers proposed previously.Yüksek LisansM.Sc

Simulation of imaging Fourier transform spectrometers using DIRSIG

Imaging Fourier Transform Spectrometers are becoming popular sensors for hyperspectral re mote sensing. To evaluate sensor design artifacts and properties, it is useful to simulate their designs using a radiometrically correct ray-tracing tool. The Digital Imaging and Remote Sensing Image Generation model allows for such design and simulation of sensor properties. Two different design types are evaluated and simulated. The first one is a Michelson-type interferometer. The sensor collects the image by operating in stare mode The interferogram is collected over time by scanning one of the mirrors to generate the required optical path difference between the signals. The second design is a triangle-path (Sagnac) interferometer. With this design, the interferogram is collected spatially on the detector array, with one spatial dimension collected in the orthogonal coordinate (Hammer, et al., 1995). The sensor is operated in pushbroom mode to collect the other spatial dimension. Simulated images and the effects of design artifacts are presented, along with the theory al lowing their understanding. The effects of design artifacts are presented both individually and in combination with other artifacts. Results of the simulation of a full scene are shown and help indi cate where those sensors can be useful. Finally, recommendations and future improvements to this research are listed

Resiliency Assessment and Enhancement of Intrinsic Fingerprinting

Intrinsic fingerprinting is a class of digital forensic technology that can detect traces left in digital multimedia data in order to reveal data processing history and determine data integrity. Many existing intrinsic fingerprinting schemes have implicitly assumed favorable operating conditions whose validity may become uncertain in reality. In order to establish intrinsic fingerprinting as a credible approach to digital multimedia authentication, it is important to understand and enhance its resiliency under unfavorable scenarios. This dissertation addresses various resiliency aspects that can appear in a broad range of intrinsic fingerprints. The first aspect concerns intrinsic fingerprints that are designed to identify a particular component in the processing chain. Such fingerprints are potentially subject to changes due to input content variations and/or post-processing, and it is desirable to ensure their identifiability in such situations. Taking an image-based intrinsic fingerprinting technique for source camera model identification as a representative example, our investigations reveal that the fingerprints have a substantial dependency on image content. Such dependency limits the achievable identification accuracy, which is penalized by a mismatch between training and testing image content. To mitigate such a mismatch, we propose schemes to incorporate image content into training image selection and significantly improve the identification performance. We also consider the effect of post-processing against intrinsic fingerprinting, and study source camera identification based on imaging noise extracted from low-bit-rate compressed videos. While such compression reduces the fingerprint quality, we exploit different compression levels within the same video to achieve more efficient and accurate identification. The second aspect of resiliency addresses anti-forensics, namely, adversarial actions that intentionally manipulate intrinsic fingerprints. We investigate the cost-effectiveness of anti-forensic operations that counteract color interpolation identification. Our analysis pinpoints the inherent vulnerabilities of color interpolation identification, and motivates countermeasures and refined anti-forensic strategies. We also study the anti-forensics of an emerging space-time localization technique for digital recordings based on electrical network frequency analysis. Detection schemes against anti-forensic operations are devised under a mathematical framework. For both problems, game-theoretic approaches are employed to characterize the interplay between forensic analysts and adversaries and to derive optimal strategies. The third aspect regards the resilient and robust representation of intrinsic fingerprints for multiple forensic identification tasks. We propose to use the empirical frequency response as a generic type of intrinsic fingerprint that can facilitate the identification of various linear and shift-invariant (LSI) and non-LSI operations

Super resolution and dynamic range enhancement of image sequences

Camera producers try to increase the spatial resolution of a camera by reducing size of sites on sensor array. However, shot noise causes the signal to noise ratio drop as sensor sites get smaller. This fact motivates resolution enhancement to be performed through software. Super resolution (SR) image reconstruction aims to combine degraded images of a scene in order to form an image which has higher resolution than all observations. There is a demand for high resolution images in biomedical imaging, surveillance, aerial/satellite imaging and high-definition TV (HDTV) technology. Although extensive research has been conducted in SR, attention has not been given to increase the resolution of images under illumination changes. In this study, a unique framework is proposed to increase the spatial resolution and dynamic range of a video sequence using Bayesian and Projection onto Convex Sets (POCS) methods. Incorporating camera response function estimation into image reconstruction allows dynamic range enhancement along with spatial resolution improvement. Photometrically varying input images complicate process of projecting observations onto common grid by violating brightness constancy. A contrast invariant feature transform is proposed in this thesis to register input images with high illumination variation. Proposed algorithm increases the repeatability rate of detected features among frames of a video. Repeatability rate is increased by computing the autocorrelation matrix using the gradients of contrast stretched input images. Presented contrast invariant feature detection improves repeatability rate of Harris corner detector around %25 on average. Joint multi-frame demosaicking and resolution enhancement is also investigated in this thesis. Color constancy constraint set is devised and incorporated into POCS framework for increasing resolution of color-filter array sampled images. Proposed method provides fewer demosaicking artifacts compared to existing POCS method and a higher visual quality in final image