Source Camera Identification using Non-decimated Wavelet Transform

Source Camera identification of digital images can be performed by matching the sensor pattern noise (SPN) of the images with that of the camera reference signature. This paper presents a non-decimated wavelet based source camera identification method for digital images. The proposed algorithm applies a non-decimated wavelet transform on the input image and split the image into its wavelet sub-bands. The coefficients within the resulting wavelet high frequency sub-bands are filtered to extract the SPN of the image. Cross correlation of the image SPN and the camera reference SPN signature is then used to identify the most likely source device of the image. Experimental results were generated using images of ten cameras to identify the source camera of the images. Results show that the proposed technique generates superior results to that of the state of the art wavelet based source camera identification

A multisensor SLAM for dense maps of large scale environments under poor lighting conditions

This thesis describes the development and implementation of a multisensor large scale autonomous mapping system for surveying tasks in underground mines. The hazardous nature of the underground mining industry has resulted in a push towards autonomous solutions to the most dangerous operations, including surveying tasks. Many existing autonomous mapping techniques rely on approaches to the Simultaneous Localization and Mapping (SLAM) problem which are not suited to the extreme characteristics of active underground mining environments. Our proposed multisensor system has been designed from the outset to address the unique challenges associated with underground SLAM. The robustness, self-containment and portability of the system maximize the potential applications.The multisensor mapping solution proposed as a result of this work is based on a fusion of omnidirectional bearing-only vision-based localization and 3D laser point cloud registration. By combining these two SLAM techniques it is possible to achieve some of the advantages of both approaches – the real-time attributes of vision-based SLAM and the dense, high precision maps obtained through 3D lasers. The result is a viable autonomous mapping solution suitable for application in challenging underground mining environments.A further improvement to the robustness of the proposed multisensor SLAM system is a consequence of incorporating colour information into vision-based localization. Underground mining environments are often dominated by dynamic sources of illumination which can cause inconsistent feature motion during localization. Colour information is utilized to identify and remove features resulting from illumination artefacts and to improve the monochrome based feature matching between frames.Finally, the proposed multisensor mapping system is implemented and evaluated in both above ground and underground scenarios. The resulting large scale maps contained a maximum offset error of ±30mm for mapping tasks with lengths over 100m

On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation

Extracting a fingerprint of a digital camera has fertile applications in image forensics, such as source camera identification and image authentication. In the last decade, Photo Response Non_Uniformity (PRNU) has been well established as a reliable unique fingerprint of digital imaging devices. The PRNU noise appears in every image as a very weak signal, and its reliable estimation is crucial for the success rate of the forensic application. In this paper, we present a novel methodical evaluation of 21 state-of-the-art PRNU estimation/enhancement techniques that have been proposed in the literature in various frameworks. The techniques are classified and systematically compared based on their role/stage in the PRNU estimation procedure, manifesting their intrinsic impacts. The performance of each technique is extensively demonstrated over a large-scale experiment to conclude this case-sensitive study. The experiments have been conducted on our created database and a public image database, the 'Dresden image databas

Grounding semantics in robots for Visual Question Answering

In this thesis I describe an operational implementation of an object detection and description system that incorporates in an end-to-end Visual Question Answering system and evaluated it on two visual question answering datasets for compositional language and elementary visual reasoning

Classification and Clustering of Shared Images on Social Networks and User Profile Linking

The ever increasing prevalence of smartphones and the popularity of social network platforms have facilitated instant sharing of multimedia content through social networks. However, the ease in taking and sharing photos and videos through social networks also allows privacy-intrusive and illegal content to be widely distributed. As such, images captured and shared by users on their profiles are considered as significant digital evidence for social network data analysis. The Sensor Pattern Noise (SPN) caused by camera sensor imperfections during the manufacturing process mainly consists of the Photo-Response Non-Uniformity (PRNU) noise that can be extracted from taken images without hacking the device. It has been proven to be an effective and robust device fingerprint that can be used for different important digital image forensic tasks, such as image forgery detection, source device identification and device linking. Particularly, by fingerprinting the camera sources captured a set of shared images on social networks, User Profile Linking (UPL) can be performed on social network platforms. The aim of this thesis is to present effective and robust methods and algorithms for better fulfilling shared image analysis based on SPN. We propose clustering and classification based methods to achieve Smartphone Identification (SI) and UPL tasks, given a set of images captured by a known number of smartphones and shared on a set of known user profiles. The important outcome of the proposed methods is UPL across different social networks where the clustered images from one social network are applied to fingerprint the related smartphones and link user profiles on the other social network. Also, we propose two methods for large-scale image clustering of different types of the shared images by users, without prior knowledge about the types and number of the smartphones

Power Spectrum Analysis of Far-IR Background Fluctuations in 160 Micron Maps From the Multiband Imaging Photometer for Spitzer

We describe data reduction and analysis of fluctuations in the cosmic far-IR background (CFIB) in observations with the Multiband Imaging Photometer for Spitzer (MIPS) instrument 160 micron detectors. We analyzed observations of an 8.5 square degree region in the Lockman Hole, part of the largest low-cirrus mapping observation with this instrument. We measured the power spectrum of the CFIB in these observations by fitting a power law to the IR cirrus component, the dominant foreground contaminant, and subtracting this cirrus signal. The CFIB power spectrum in the range 0.2 arc min^{-1} <k< 0.5 arc min^{-1} is consistent with previous measurements of a relatively flat component. However, we find a large power excess at low k, which falls steeply to the flat component in the range 0.03 arc min^{-1} <k< 0.1 arc min^{-1}. This low-k power spectrum excess is consistent with predictions of a source clustering "signature". This is the first report of such a detection in the far-IR.Comment: This is the version of the paper accepted by A&A, which includes various changes and new material. The superior-quality PDF with integrated figures may be downloaded at http://www-astro.lbl.gov/~bruce/spitzerpaper1/cfibaa_pub.pdf 15 pages, figures integrated with text. This paper supersedes astro-ph/050416

An improved pipeline for LC-MS spectral processing and annotation.

Mass spectrometry coupled to liquid chromatography (LC-MS) is routinely used for metabolomics studies. While steps in data acquisition are fairly standardised and automated, structural metabolite identification still depends on manual curation and expert knowledge, forming a major bottleneck in LC-MS based pipelines. The work presented in this thesis represents a novel data processing strategy, which aids metabolite identification through deliberate us of the the correlation structure that exists between spectral features, as well as chromatographic profile and data acquisition order. This strategy aligns features originating from the same chemical entity across all samples as a group, ensuring that chemically-related features are accurately aligned despite fluctuations in the chromatographic and mass spectrometric measurements occurring during the experimental run time. Spectral features aligned in this way are consequently matched to in-house chemical standards databases more efficiently and accurately, on account of the retained and chemically-relevant spectral information. This pipeline has been developed and is presented as an open-source R package - massFlowR. This thesis demonstrates the utility of massFlowR with simulated data, as well as an open-source urine metabolomics study DEVSET, and a large-scale cohort study AIRWAVE, where the performance of massFlowR is compared with the widely-used package XCMS.Open Acces