Defense Notices

Quantum computing is a promising technology that can potentially demonstrate supremacy over classical computing in solving specific problems. At present, two critical challenges for quantum computing are quantum state decoherence, and low scalability of current quantum devices. Decoherence places constraints on realistic applicability of quantum algorithms as real-life applications usually require complex equivalent quantum circuits to be realized. For example, encoding classical data on quantum computers for solving I/O and data-intensive applications generally requires quantum circuits that violate decoherence constraints. In addition, current quantum devices are of small-scale having low quantum bit(qubit) counts, and often producing inaccurate or noisy measurements, which also impacts the realistic applicability of real-world quantum algorithms. Consequently, benchmarking of existing quantum algorithms and investigation of new applications are heavily dependent on classical simulations that use costly, resource-intensive computing platforms. Hardware-based emulation has been alternatively proposed as a more cost-effective and power-efficient approach. This work proposes a hardware-based emulation methodology for quantum algorithms, using cost-effective Field-Programmable Gate-Array(FPGA) technology. The proposed methodology consists of three components that are required for complete emulation of quantum algorithms; the first component models classical-to-quantum(C2Q) data encoding, the second emulates the behavior of quantum algorithms, and the third models the process of measuring the quantum state and extracting classical information, i.e., quantum-to-classical(Q2C) data decoding. The proposed emulation methodology is used to investigate and optimize methods for C2Q/Q2C data encoding/decoding, as well as several important quantum algorithms such as Quantum Fourier Transform(QFT), Quantum Haar Transform(QHT), and Quantum Grover’s Search(QGS). This work delivers contributions in terms of reducing complexities of quantum circuits, extending and optimizing quantum algorithms, and developing new quantum applications. For higher emulation performance and scalability of the framework, hardware design techniques and hardware architectural optimizations are investigated and proposed. The emulation architectures are designed and implemented on a high-performance-reconfigurable-computer(HPRC), and proposed quantum circuits are implemented on a state-of-the-art quantum processor. Experimental results show that the proposed hardware architectures enable emulation of quantum algorithms with higher scalability, higher accuracy, and higher throughput, compared to existing hardware-based emulators. As a case study, quantum image processing using multi-spectral images is considered for the experimental evaluations. 

Open-source intelligence is a branch within cybercrime investigation that focuses on information collection and aggregation. Through this aggregation, investigators and analysts can analyze the data for connections relevant to the investigation. There are many tools that assist with information collection and aggregation. However, these often require enterprise licensing. A solution to enterprise licensed tools is using open-source tools to collect information, often by scraping websites. These tools provide useful information, but they provide a large number of disjointed reports. The framework we developed automates information collection, aggregates these reports, and generates one single graphical report. By using a graphical report, the time required for analysis is also reduced. This framework can be used for different investigations. We performed a case study regarding the performance of the framework with missing person case information. It showed a significant improvement in the time required for information collection and report analysis. 

Word embedding has become a popular form of data representation that is used to train deep neural networks in many natural

language processing tasks, such as Machine Translation, Question Answer Generation, Named Entity Recognition, Next

Word/Sentence Prediction etc. With embedding, each word is represented as a dense vector which captures its semantic relationship

with other words and can better empower Machine Learning models to achieve state-of-the-art performance.

However, due to the memory and time intensive nature of learning such word embeddings, transfer learning has emerged as a

common practice to warm start the training process. As a result, an efficient way is to initialize with pretrained word vectors and then

fine-tune those on downstream domain specific smaller datasets. This study aims to find whether we can infer the contextual

distribution (i.e., how words cooccur in a sentence driven by syntactic regularities) of the downstream datasets given that we have

access to the embeddings from both pre-training and fine-tuning processes.

In this work, we propose a focused sampling method along with a novel model inversion architecture “Invernet” to invert word

embeddings into the word-to-word context information of the fine-tuned dataset. We consider the popular word2Vec models

including CBOW, SkipGram, and GloVe based algorithms with various unsupervised settings. We conduct extensive experimental

study on two real-world news datasets: Antonio Gulli’s News Dataset from Hugging Face repository and a New York Times dataset

from both quantitative and qualitative perspectives. Results show that “Invernet” has been able to achieve an average F1 score of 0.75

and an average AUC score of 0.85 in an attack scenario.

A concerning pattern from our experiments reveal that embedding models that are generally considered superior in different tasks

tend to be more vulnerable to model inversion. Our results suggest that a significant amount of context distribution information from

the downstream dataset can potentially leak if an attacker gets access to the pretrained and fine-tuned word embeddings. As a result,

attacks using “Invernet” can jeopardize the privacy of the users whose data might have been used to fine-tune the word embedding

model.

With the growing computational power and the enormous data available from many sectors, applications with machine learning (ML) components are widely adopted in our everyday lives. One major drawback associated with ML models is hard to guarantee same performance with changing environment. Since ML models are not traditional software that can be tested end-to-end. ML models are vulnerable against distributional shifts and cyber-attacks. Various cyber-attacks against deep neural networks (DNN) have been proposed in the literature, such as poisoning, evasion, backdoor, and model inversion. In the evasion attacks against DNN, the attacker generates adversarial instances that are visually indistinguishable from benign samples and sends them to the target DNN to trigger misclassifications.

In our work, we proposed a novel multi-view adversarial image detector, namely ‘Argos’, based on a novel observation. That is, there exist two” souls” in an adversarial instance, i.e., the visually unchanged content, which corresponds to the true label, and the added invisible perturbation, which corresponds to the misclassified label. Such inconsistencies could be further amplified through an autoregressive generative approach that generates images with seed pixels selected from the original image, a selected label, and pixel distributions learned from the training data. The generated images (i.e., the “views”) will deviate significantly from the original one if the label is adversarial, demonstrating inconsistencies that ‘Argos’ expects to detect. To this end, ‘Argos’ first amplifies the discrepancies between the visual content of an image and its misclassified label induced by the attack using a set of regeneration mechanisms and then identifies an image as adversarial if the reproduced views deviate to a preset degree. Our experimental results show that ‘Argos’ significantly outperforms two representative adversarial detectors in both detection accuracy and robustness against six well-known adversarial attacks.

Coq's extraction plugin is used to produce code of a general purpose

  programming language from a specification written in the Calculus of Inductive

  Constructions (CIC). Currently, this mechanism is trusted, since there is no

  formal connection between the synthesized code and the CIC terms it originated

  from. This comes from a lack of formal specifications for the target

  languages: OCaml, Haskell, and Scheme. We intend to use the formally specified

  CakeML language as an extraction target, and generate a theorem in Coq that

  relates the generated CakeML abstract syntax to the CIC terms it is generated

  from. This work expands on the techniques used in the HOL4 translator from

  Higher Order Logic to CakeML. The HOL4 translator also allows for the

  generation of stateful code from the state and exception monad. We expand on

  their techniques by extracting terms with dependent types, and generating

  stateful code for other kinds of monads, like the reader monad, depending on

  what kind of computation the monad intends to represent.

Remote attestation is a process where one digital system gathers and provides evidence of its state and identity to an external system. For this process to be successful, the external system must find the evidence convincingly trustworthy within that context. Remote attestation is difficult to make trustworthy due to the external system’s limited access to the attestation target. In contrast to local attestation, the appraising system is unable to directly observe and oversee the attestation target. In this work, we present a system architecture design and prototype implementation that we claim enables trustworthy remote attestation. Furthermore, we formally model the system within a temporal logic embedded in the Coq theorem prover and present key theorems that strengthen this trust argument.

Recent years have seen tremendous developments in the field of computer vision and its extensive applications. The fundamental task, image classification, benefiting from deep convolutional neural networks (CNN)'s extraordinary ability to extract deep semantic information from input data, has become the backbone for many other computer vision tasks, like object detection and segmentation. A modern detection usually has bounding-box regression and class prediction with a pre-trained classification model as the backbone. The architecture is proven to produce good results, however, improvements can be made with closer inspections. A detector takes a pre-trained CNN from the classification task and selects the final bounding boxes from multiple proposed regional candidates by a process called non-maximum suppression (NMS), which picks the best candidates by ranking their classification confidence scores. The localization evaluation is absent in the entire process. Another issue is the classification uses one-hot encoding to label the ground truth, resulting in an equal penalty for misclassifications between any two classes without considering the inherent relations between the classes.

My research aims to address the following issues. (1) We proposed the first location-aware detection framework for single-shot detectors that can be integrated into any single-shot detectors. It boosts detection performance by calibrating the ranking process in NMS with localization scores. (2) To more effectively back-propagate gradients, we designed a super-class guided architecture that consists of a superclass branch (SCB) and a finer class branch (FCB). To further increase the effectiveness, the features from SCB with high-level information are fed to FCB to guide finer class predictions. (3) Recent works have shown 3D point cloud models are extremely vulnerable under adversarial attacks, which poses a serious threat to many critical applications like autonomous driving and robotic controls. To increase the robustness of CNN models on 3D point cloud models, we propose a family of robust structured declarative classifiers for point cloud classification, where the internal constrained optimization mechanism can effectively defend adversarial attacks through implicit gradients.

Intelligent Reflecting Surfaces (IRSs) grant the ability to control what was once considered the uncontrollable part of wireless communications, the channel. These smart signal mirrors show promise to significantly improve the effective signal-to-noise-ratio (SNR) of cell-users when the line-of-sight (LOS) channel between the base station (BS) and user is blocked. IRSs use implementable optimized phase shifts that beamform a reflected signal around channel blockages, and because they are passive devices, they have the benefit of having low cost and low power consumption. Previous works have concluded that IRSs need several hundred elements to outperform relays. Unfortunately, overhead and complexity costs related to optimizing these devices limit their scope to single-input single-output (SISO) systems. With multiple-input multiple-output (MIMO) and Massive MIMO becoming crucial components to modern 5G and beyond networks, a way to mitigate these overhead costs and integrate IRS technology with the promising MIMO techniques is paramount for these devices to have a place within modern cell technologies. This thesis proposes an IRS element grouping scheme that greatly reduces the number of unique IRS phases that need to be calculated and sent to the IRS controller via the limited rate feedback channel and allows for the ideal number of groups to be obtained at the BS before data transmission. Three methods are proposed to design the phase shifts and element partitioning within our scheme to improve effective SNR in an IRS-aided system. In our simulations, it is shown that our best performing method is one that dynamically partitions the IRS elements into non- uniform groups based on information gathered from the reflected channel and then optimizes its phase shifts. This method successfully handles the overhead trade-off problem, and shows significant achievable rate improvement from previous works.

Multichannel synthetic aperture radar (SAR) ice sounders rely on parametric angle estimators in tomography to resolve elevation angle beyond the Rayleigh resolution limit of their cross-track arrays. The potential super resolution capability of these techniques is predicated on perfect knowledge of the array’s response to directional sources, referred to as the array manifold. Array manifold calibration improves angle estimator performance by reducing the mismatch between the model of the array’s transfer function and truth; its study straddles the fields of both signal processing and antenna theory, yet associated literature reveals dichotomous methodologies that perpetuate fragmented interpretations of the manifold calibration problem. This dissertation addresses calibration for SAR ice sounders that three dimensionally image ice sheet and glacier beds with tomographic techniques. The approach is rooted in array signal processing first but seeks a more unifying perspective of the manifold calibration problem by leveraging commercial computational electromagnetics software to understand error mechanisms and algorithm performance with a deterministic model of an electromagnetic manifold. The research outlined here proposes creation of large snapshot databases that aid in identifying calibration targets in SAR pixels with known arrival angles. The signal processing methodology taxonomizes manifold calibration into parametric and nonparametric forms and advances both in the context of SAR sounders. A parametric estimator of nonlinear manifold parameters that are common across disjoint sets is derived. The algorithm framework capitalizes on a snapshot database to aggregate many angularly diverse observations in estimating unknown model parameters. The technique, which handles multitarget calibration, is desirable in the SAR sounder problem but requires a parametric model of the angle-dependent manifold. Nonparametric calibration techniques characterize the array response over the field of view but require many observations of single sources over dense calibration grids. A subspace clustering technique is proposed to identify snapshots with a single dominant source, thereby enabling a principal components-based characterization of the sounder manifold. The measured manifold leads to significant performance improvements over the traditional array response model in tomography. These results indicate that manifold calibration will reduce uncertainty in sounder-derived maps of the subsurface, leading to more accurate estimates of total fresh ice volume.

Over the last few years, one of the primary focuses in engineering development has been system packaging and miniaturization. This is apparent in various areas such as the rise of Internet of Things (IoT), CubeSats, and Unmanned Aerial Systems (UAS). The simultaneous miniaturization in multiple industries has enabled advancements in remote sensing instrument development. Sensors such as radars, lidars, and cameras are used on UAS to characterize various aspects of the Earth System like ice, soil, and vegetation, thereby improving our understanding. In this work, an Ultra-wideband (UWB) radar system design for the Vapor 55 UAS rotorcraft is investigated. A compact, lightweight 2 – 18 GHz Frequency Modulated Continuous Wave (FMCW) radar with two channels on transmit and receive is designed to characterize extended targets like soil and snow. This thesis reports initial proof-of-concept field measurements performed with soil as the target to identify backscatter signatures that are indicative of moisture content. The thesis also describes the exploratory design, development, and laboratory test results of the miniaturized radar electronics and compact antenna front-end.