Department of Applied Computer Science
https://hdl.handle.net/10680/1602
2024-03-19T11:52:14ZCryptographic Techniques for Data Privacy in Digital Forensics
https://hdl.handle.net/10680/2131
Cryptographic Techniques for Data Privacy in Digital Forensics
Ogunseyi, Taiwo Blessing; Oluwasola, Mary Adedayo
The acquisition and analysis of data in digital forensics raise different data privacy challenges. Many existing works on digital forensic readiness discuss what information should be stored and how to collect relevant data to facilitate investigations. However, the cost of this readiness often directly impacts the privacy of innocent third parties and suspects if the collected information is irrelevant. Approaches that have been suggested for privacy-preserving digital forensics focus on the use of policy, non-cryptography-based, and cryptography-based solutions. Cryptographic techniques have been proposed to address issues of data privacy during data analysis. As the utilization of some of these cryptographic techniques continues to increase, it is important to evaluate their applicability and challenges in relation to digital forensics processes. This study provides digital forensics investigators and researchers with a roadmap to understanding the data privacy challenges in digital forensics and examines the various privacy techniques that can be utilized to tackle these challenges. Specifically, we review the cryptographic techniques applied for privacy protection in digital forensics and categorize them within the context of whether they support trusted third parties, multiple investigators, and multi-keyword searches. We highlight some of the drawbacks of utilizing cryptography-based methods in privacy-preserving digital forensics and suggest potential solutions to the identified shortcomings. In addition, we propose a conceptual privacy-preserving digital forensics (PPDF) model that is based on the use of cryptographic techniques and analyze the model within the context of the above-mentioned factors. An evaluation of the model is provided through a consideration of identified factors that may affect an investigation. Lastly, we provide an analysis of how existing principles for preserving privacy in digital forensics are addressed in our PPDF model. Our evaluation shows that the model aligns with many of the existing privacy principles recommended for privacy protection in digital forensics.
2023-12-15T00:00:00ZA comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images
https://hdl.handle.net/10680/2114
A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images
Noshiri, Nooshin; Beck, Michael A.; Bidinosti, Christopher P.; Henry, Christopher J.
Hyperspectral imaging (HSI) is a non-destructive and contactless technology that provides valuable information about the structure and composition of an object. It has the ability to capture detailed information about the chemical and physical properties of agricultural crops. Due to its wide spectral range, compared with multispectral-or RGB-based imaging methods, HSI can be a more effective tool for monitoring crop health and productivity. With the advent of this imaging tool in agrotechnology, researchers can more accurately address issues related to the detection of diseased and defective crops in the agriculture industry. This allows to implement the most suitable and accurate farming solutions, such as irrigation and fertilization, before crops enter a damaged and difficult-to-recover phase of growth in the field. While HSI provides valuable insights into the object under investigation, the limited number of HSI datasets for crop evaluation presently poses a bottleneck. Dealing with the curse of dimensionality presents another challenge due to the abundance of spectral and spatial information in each hyperspectral cube. State-of-the-art methods based on 1D and 2D convolutional neural networks (CNNs) struggle to efficiently extract spectral and spatial information. On the other hand, 3D-CNN-based models have shown significant promise in achieving better classification and detection results by leveraging spectral and spatial features simultaneously. Despite the apparent benefits of 3D-CNN-based models, their usage for classification purposes in this area of research has remained limited. This paper seeks to address this gap by reviewing 3D-CNN-based architectures and the typical deep learning pipeline, including preprocessing and visualization of results, for the classification of hyperspectral images of diseased and defective crops. Furthermore, we discuss open research areas and challenges when utilizing 3D-CNNs with HSI data.
2023-09-14T00:00:00ZInside out: transforming images of lab-grown plants for machine learning applications in agriculture
https://hdl.handle.net/10680/2087
Inside out: transforming images of lab-grown plants for machine learning applications in agriculture
Krosney, Alexander E.; Sotoodeh, Parsa; Henry, Christopher J.; Beck, Michael A.; Bidinosti, Christopher P.
Machine learning tasks often require a significant amount of training data for the resultant network to perform suitably for a given problem in any domain. In agriculture, dataset sizes are further limited by phenotypical differences between two plants of the same genotype, often as a result of different growing conditions. Synthetically-augmented datasets have shown promise in improving existing models when real data is not available.
2023-07-06T00:00:00ZLeveraging Guided Backpropagation to Select Convolutional Neural Networks for Plant Classification
https://hdl.handle.net/10680/2000
Leveraging Guided Backpropagation to Select Convolutional Neural Networks for Plant Classification
Mostafa, Sakib; Mondal, Debajyoti; Beck, Michael A.; Bidinosti, Christopher P.; Henry, Christopher J.; Stavness, Ian
The development of state-of-the-art convolutional neural networks (CNN) has allowed researchers to perform plant classification tasks previously thought impossible and rely on human judgment. Researchers often develop complex CNN models to achieve better performances, introducing over-parameterization and forcing the model to overfit on a training dataset. The most popular process for evaluating overfitting in a deep learning model is using accuracy and loss curves. Train and loss curves may help understand the performance of a model but do not provide guidance on how the model could be modified to attain better performance. In this article, we analyzed the relation between the features learned by a model and its capacity and showed that a model with higher representational capacity might learn many subtle features that may negatively affect its performance. Next, we showed that the shallow layers of a deep learning model learn more diverse features than the ones learned by the deeper layers. Finally, we propose SSIM cut curve, a new way to select the depth of a CNN model by using the pairwise similarity matrix between the visualization of the features learned at different depths by using Guided Backpropagation. We showed that our proposed method could potentially pave a new way to select a better CNN model.
2022-05-11T00:00:00Z