A Feature Comparison of Modern Digital Forensic Imaging Software
- URL: http://arxiv.org/abs/2001.00301v1
- Date: Thu, 2 Jan 2020 02:42:31 GMT
- Title: A Feature Comparison of Modern Digital Forensic Imaging Software
- Authors: Jiyoon Ham, Joshua I. James
- Abstract summary: Fundamental processes in digital forensic investigation, such as disk imaging, were developed when digital investigation was relatively young.
We show the weakness in current digital investigation fundamental software development and maintenance over time.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Fundamental processes in digital forensic investigation, such as disk
imaging, were developed when digital investigation was relatively young. As
digital forensic processes and procedures matured, these fundamental tools,
that are the pillars of the reset of the data processing and analysis phases of
an investigation, largely stayed the same. This work is a study of modern
digital forensic imaging software tools. Specifically, we will examine the
feature sets of modern digital forensic imaging tools, as well as their
development and release cycles to understand patterns of fundamental tool
development. Based on this survey, we show the weakness in current digital
investigation fundamental software development and maintenance over time. We
also provide recommendations on how to improve fundamental tools.
Related papers
- From Digital Twins to Digital Twin Prototypes: Concepts, Formalization,
and Applications [55.57032418885258]
There is no consensual definition of what a digital twin is.
Our digital twin prototype (DTP) approach supports engineers during the development and automated testing of embedded software systems.
arXiv Detail & Related papers (2024-01-15T22:13:48Z) - Harnessing Machine Learning for Discerning AI-Generated Synthetic Images [2.6227376966885476]
We employ machine learning techniques to discern between AI-generated and genuine images.
We refine and adapt advanced deep learning architectures like ResNet, VGGNet, and DenseNet.
The experimental results were significant, demonstrating that our optimized deep learning models outperform traditional methods.
arXiv Detail & Related papers (2024-01-14T20:00:37Z) - An Innovative Tool for Uploading/Scraping Large Image Datasets on Social
Networks [9.27070946719462]
We propose an automated approach by means of a digital tool that we created on purpose.
The tool is capable of automatically uploading an entire image dataset to the desired digital platform and then downloading all the uploaded pictures.
arXiv Detail & Related papers (2023-11-01T23:27:37Z) - Computer Vision on X-ray Data in Industrial Production and Security
Applications: A survey [89.45221564651145]
This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications.
It covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets.
arXiv Detail & Related papers (2022-11-10T13:37:36Z) - Robustar: Interactive Toolbox Supporting Precise Data Annotation for
Robust Vision Learning [53.900911121695536]
We introduce the initial release of our software Robustar.
It aims to improve the robustness of vision classification machine learning models through a data-driven perspective.
arXiv Detail & Related papers (2022-07-18T21:12:28Z) - CapillaryX: A Software Design Pattern for Analyzing Medical Images in
Real-time using Deep Learning [0.688204255655161]
This paper provides a computing architecture that locally and in parallel can analyze medical images in real-time.
We focus on a specific medical-industrial case study, namely the quantifying of blood vessels in microcirculation images.
Our results show that our system is approximately 78% faster than its serial system counterpart and 12% faster than a master-slave parallel system architecture.
arXiv Detail & Related papers (2022-04-13T18:47:04Z) - Automatic Image Content Extraction: Operationalizing Machine Learning in
Humanistic Photographic Studies of Large Visual Archives [81.88384269259706]
We introduce Automatic Image Content Extraction framework for machine learning-based search and analysis of large image archives.
The proposed framework can be applied in several domains in humanities and social sciences.
arXiv Detail & Related papers (2022-04-05T12:19:24Z) - A New Approach for Image Authentication Framework for Media Forensics
Purpose [0.0]
This paper introduces a novel digital forensic security framework for digital image authentication and originality identification.
The approach depends on implanting secret code into RGB images that should indicate any unauthorized modification on the image under investigation.
arXiv Detail & Related papers (2021-10-03T18:31:37Z) - A Comprehensive Survey on Image Dehazing Based on Deep Learning [89.77554550654227]
The presence of haze significantly reduces the quality of images.
Researchers have designed a variety of algorithms for image dehazing (ID) to restore the quality of hazy images.
There are few studies that summarize the deep learning (DL) based dehazing technologies.
arXiv Detail & Related papers (2021-06-07T03:51:25Z) - Automated Artefact Relevancy Determination from Artefact Metadata and
Associated Timeline Events [7.219077740523683]
Case-hindering, multi-year digital forensic evidence backlogs have become commonplace in law enforcement agencies throughout the world.
This is due to an ever-growing number of cases requiring digital forensic investigation coupled with the growing volume of data to be processed per case.
Leveraging previously processed digital forensic cases and their component artefact relevancy classifications can facilitate an opportunity for training automated artificial intelligence based evidence processing systems.
arXiv Detail & Related papers (2020-12-02T14:14:26Z) - Image Segmentation Using Deep Learning: A Survey [58.37211170954998]
Image segmentation is a key topic in image processing and computer vision.
There has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models.
arXiv Detail & Related papers (2020-01-15T21:37:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.