Global ETD Search

1	On the preservation of media trustworthiness in the social media era Lago, Federica 29 March 2022 (has links) The amount of multimedia content shared everyday online recently underwent a dramatic increase. This, combined with the stunning realism of fake images that can be generated with AI-based technologies, undermines the trustworthiness of online information sources. In this work, we tackle the problem of preserving media trustworthiness online from two different points of view. The first one consists in assessing the human ability to spot fake images, focusing in particular on synthetic faces, which are extremely realistic and can represent a severe threat if used to disseminate fake news. A perception study allowed us to prove for the first time how people are more prone to question the reality of authentic pictures rather than the one of last-generation AI-generated images. Secondly, we focused on social media forensics: our goal is to reconstruct the history of an image shared or re-shared online as typically happens nowadays. We propose a new framework that is able to trace the history of an image over multiple sharings. This framework improves the state of the art and has the advantage of being easily extensible with new methods and thus adapt to new datasets and scenarios. In fact, in this environment of fast-paced technological evolution, being able to adapt is fundamental to preserve our trust in what we see.
2	IMAGE ANALYSIS FOR SHADOW DETECTION, SATELLITE IMAGE FORENSICS AND EATING SCENE SEGMENTATION AND CLUSTERING Sri Kalyan Yarlagadda (9722306) 15 December 2020 (has links) Recent advances in machine learning has enabled notable progress in many aspects of image analysis. In this thesis, we present three applications to exemplify such advancement, including shadow detection, satellite image forensics and eating scene segmentation and clustering. Shadow detection and removal are of great interest to the image processing and image forensics community. In this thesis, we study automatic shadow detection from two different perspectives. First, we propose automatic methods for detecting and removing shadows in color images. Second, we present machine learning based methods to detect if shadows have been removed in an image. In the second part of the thesis, we study image forensics for satellite images. Satellite images have been subjected to various tampering and manipulations due to easy access and the availability of image manipulation tools. In this thesis, we propose methods to automatically detect and localize spliced objects in satellite images. Extracting information from the eating scene captured by images provides new means of studying the relationship between diet and health. In the third part of the thesis, we propose a class-agnostic food segmentation method that is able to segment foods without knowing the food type and a method to cluster eating scene images based on the eating environment. Computer Vision Deep Learning Image Segmentation Image Analysis Scene Clustering Media Forensics
3	Digital Forensic Analysis of Snapchat and BeReal : In Search of Artifacts Persson, Philip January 2023 (has links) Snapchat and BeReal are popular social media platforms focused on photo sharing and instant messaging. A tool often used in police investigations is the analysis of communication, this includes different electronic devices and smartphone devices. However, Law enforcement faces challenges when analyzing communication in police investigations due to encryption and privacy protection. The experiment included three phases: artifact production, data acquisition, and data examination & analysis. In the artifact production phase, four devices exchanged chat messages, images, and videos. The data acquisition phase involved using two licensed forensic tools, Magnet Axiom and MOBILedit Forensic PRO. The final phase involved examining and analyzing the extracted data to find artifacts that could serve as supporting evidence in criminal investigations. Several conclusions were drawn from this study. Notably, the experiment revealed diverse types of forensic artifacts. Metadata files that contained information about the applications were the most common. Examples of this were com.snapchat.android.apk and com.bereal.ft.apk for Android, and iTunesMetadata.plist together with other .plist files for iPhone. These files provide valuable data such as user information, activity, and timestamps. Important locations and key factors were also identified. Digital Forensics Snapchat BeReal Social media forensics Computer and Information Sciences Data- och informationsvetenskap
4	Media Forensics Using Machine Learning Approaches David Güera (7534550) 30 October 2019 (has links) <div>Consumer-grade imaging sensors have become ubiquitous in the past decade. Images and videos, collected from such sensors are used by many entities for public and private communications, including publicity, advocacy, disinformation, and deception. </div><div>In this thesis, we present tools to be able to extract knowledge from and understand this imagery and its provenance. Many images and videos are modified and/or manipulated prior to their public release. We also propose a set of forensics and counter-forensic techniques to determine the integrity of this multimedia content and modify it in specific ways to deceive adversaries. The presented tools are evaluated using publicly available datasets and independently organized challenges.</div> machine learning image processing computer vision deep learning media forensics deep fakes artificial intelligence
5	<b>Forensic Analysis of Images and Documents</b> Ruiting Shao (18018187) 23 February 2024 (has links) <p dir="ltr">This thesis involves three topics related to forensic analysis of media data. The first topic is the analysis of images and documents that have been created with a scanner. The goal is to detect and identify scanner model from the scanned images/documents. We propose a deep learning system that can automatically learn the inherent features of the scanned images. This system will produce a scanner model identification and a reliability map for a scanned image. The proposed system has shown promising results in the forensic analysis of scanned images. The second topic is related to forensic integrity of scientific papers. The project is divided into multiple tasks, data collection, image extraction, and manipulation detection. We have constructed a dataset of retracted scientific papers that have been verified to have issues with integrity. We design and maintain a web-based Scientific Integrity System for forensic analysis of the images within scientific publications. The third topic is related to media document analysis. Our goal is to identify the publication style for media document, aiding in the potential document manipulation. We are mainly focusing on image-text consistency check, and synthetic tweets analysis. For image-text inconsistency check, we describe a system that can examine an image in document and the corresponding text caption (or other associated text with the image) to check the image/text consistency. For synthetic tweets analysis, we propose a system to detect and identify the text generation models and paraphrase attack models.</p> Natural language processing Computer vision Image processing Multimodal analysis and synthesis Digital forensics Deep learning scanner detection synthetic text analysis media forensics natural language generation (NLG) author attribution image-text consistency social media misinformations scientific integrity Person of Interest
6	An Adversarial Approach to Spliced Forgery Detection and Localization in Satellite Imagery Emily R Bartusiak (6630773) 11 June 2019 (has links) The widespread availability of image editing tools and improvements in image processing techniques make image manipulation feasible for the general population. Oftentimes, easy-to-use yet sophisticated image editing tools produce results that contain modifications imperceptible to the human observer. Distribution of forged images can have drastic ramifications, especially when coupled with the speed and vastness of the Internet. Therefore, verifying image integrity poses an immense and important challenge to the digital forensic community. Satellite images specifically can be modified in a number of ways, such as inserting objects into an image to hide existing scenes and structures. In this thesis, we describe the use of a Conditional Generative Adversarial Network (cGAN) to identify the presence of such spliced forgeries within satellite images. Additionally, we identify their locations and shapes. Trained on pristine and falsified images, our method achieves high success on these detection and localization objectives. Image Processing Image Processing Machine Learning Electrical Engineering Computer Engineering Electrical and Computer Engineering ECE Satellite Imagery cGAN Media Forensics Digital Forensics Image Forensic Analysis Spliced Forgery Spliced Objects Splicing Doctored Images Image Manipulation Image Editing Falsified Imagery Forged Images Forgery Localization Forgery Detection
7	Machine Learning for Speech Forensics and Hypersonic Vehicle Applications Emily R Bartusiak (6630773) 06 December 2022 (has links) <p>Synthesized speech may be used for nefarious purposes, such as fraud, spoofing, and misinformation campaigns. We present several speech forensics methods based on deep learning to protect against such attacks. First, we use a convolutional neural network (CNN) and transformers to detect synthesized speech. Then, we investigate closed set and open set speech synthesizer attribution. We use a transformer to attribute a speech signal to its source (i.e., to identify the speech synthesizer that created it). Additionally, we show that our approach separates different known and unknown speech synthesizers in its latent space, even though it has not seen any of the unknown speech synthesizers during training. Next, we explore machine learning for an objective in the aerospace domain.</p> <p><br></p> <p>Compared to conventional ballistic vehicles and cruise vehicles, hypersonic glide vehicles (HGVs) exhibit unprecedented abilities. They travel faster than Mach 5 and maneuver to evade defense systems and hinder prediction of their final destinations. We investigate machine learning for identifying different HGVs and a conic reentry vehicle (CRV) based on their aerodynamic state estimates. We also propose a HGV flight phase prediction method. Inspired by natural language processing (NLP), we model flight phases as “words” and HGV trajectories as “sentences.” Next, we learn a “grammar” from the HGV trajectories that describes their flight phase transition patterns. Given “words” from the initial part of a HGV trajectory and the “grammar”, we predict future “words” in the “sentence” (i.e., future HGV flight phases in the trajectory). We demonstrate that this approach successfully predicts future flight phases for HGV trajectories, especially in scenarios with limited training data. We also show that it can be used in a transfer learning scenario to predict flight phases of HGV trajectories that exhibit new maneuvers and behaviors never seen before during training.</p> Audio processing Computer vision Digital forensics Deep learning machine learning deep learning speech forensics media forensics convolutional neural network transformer convolutional transformer ensemble spectrogram analysis mel spectrogram analysis synthesized speech detection synthesized speech attribution closed set open set t-stochastic neighbor embedding latent space analysis hypersonics hypersonic glide vehicles vehicle classification flight phase prediction stochastic grammar k-nearest neighbors support vector machine probabilistic context-free grammar automatic distillation of structure generalized earley parser transfer learning

1

Page generated in 0.0691 seconds