Publications

Abstract not provided.

Security assessments support decision-makers' ability to evaluate current capabilities of high consequence facilities (HCF) to respond to possible attacks. However, increasing complexity of today's operational environment requires a critical review of traditional approaches to ensure that implemented assessments are providing relevant and timely insights into security of HCFs. Using interviews and focus groups with diverse subject matter experts (SMEs), this study evaluated the current state of security assessments and identified opportunities to achieve a more "ideal" state. The SME-based data underscored the value of a systems approach for understanding the impacts of changing operational designs and contexts (as well as cultural influences) on security to address methodological shortcomings of traditional assessment processes. These findings can be used to inform the development of new approaches to HCF security assessments that are able to more accurately reflect changing operational environments and effectively mitigate concerns arising from new adversary capabilities.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Protecting high consequence facilities (HCF) from malicious attacks is challenged by today’s increasingly complex, multi-faceted, and interdependent operational environments and threat domains. Building on current approaches, insights from complex systems and network science can better incorporate multidomain interactions observed in HCF security operations. These observations and qualitative HCF security expert data support invoking a multilayer modeling approach for HCF security to shift from a “reactive” to a “proactive” paradigm that better explores HCF security dynamics and resilience not captured in traditional approaches. After exploring these multi-domain interactions, this paper introduces how systems theory and network science insights can be leveraged to describe HCF security as complex, interdependent multilayer directed networks. A hypothetical example then demonstrates the utility of such an approach, followed by a discussion on key insights and implications of incorporating multilayer network analytical performance measures into HCF security.

Existing security models are highly linear and fail to capture the rich interactions that occur across security technology, infrastructure, cybersecurity, and human/organizational components. In this work, we will leverage insights from resilience science, complex system theory, and network theory to develop a next-generation security model based on these interactions to address challenges in complex, nonlinear risk environments and against innovative and disruptive technologies. Developing such a model is a key step forward toward a dynamic security paradigm (e.g., shifting from detection to anticipation) and establishing the foundation for designing next-generation physical security systems against evolving threats in uncontrolled or contested operational environments.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Many optical systems are used for specific tasks such as classification. Of these systems, the majority are designed to maximize image quality for human observers. However, machine learning classification algorithms do not require the same data representation used by humans. We investigate the compressive optical systems optimized for a specific machine sensing task. Two compressive optical architectures are examined: an array of prisms and neutral density filters where each prism and neutral density filter pair realizes one datum from an optimized compressive sensing matrix, and another architecture using conventional optics to image the aperture onto the detector, a prism array to divide the aperture, and a pixelated attenuation mask in the intermediate image plane. We discuss the design, simulation, and trade-offs of these systems built for compressed classification of the Modified National Institute of Standards and Technology dataset. Both architectures achieve classification accuracies within 3% of the optimized sensing matrix for compression ranging from 98.85% to 99.87%. The performance of the systems with 98.85% compression were between an F / 2 and F / 4 imaging system in the presence of noise.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Unmanned aircraft systems (UASs) have grown significantly within the private sector with ease of acquisition and platform capabilities far outstretching what previously existed. Where once the operation of these platforms was limited to skilled individuals, increased computational power, manufacturing techniques, and increased autonomy allows inexperienced individuals to skillfully maneuver these devices. With this rise in consumer use of UAS comes an increased security concern regarding their use for malicious intent.The focus area of counter UAS (CUAS) remains a challenging space due to a small cross-sectioned UAS's ability to move in all three dimensions, attain very high speeds, carry payloads of notable weight, and avoid standard delay techniques.We examine frequency analysis of pixel fluctuation over time to exploit the temporal frequency signature present in UAS imagery. This signature allows for lower pixels-on-target detection [1]. The methodology also acts as a method of assessment due to the distinct frequency signatures of UAS when examined against the standard nuisance alarms such as birds. The temporal frequency analysis (TFA) method demonstrates a UAS detection and assessment method. In this paper we discuss signal processing and Fourier filter optimization methodologies that increase UAS contrast.

Images are often not the optimal data form to perform machine learning tasks such as scene classification. Compressive classification can reduce the size, weight, and power of a system by selecting the minimum information while maximizing classification accuracy.In this work we present designs and simulations of prism arrays which realize sensing matrices using a monolithic element. The sensing matrix is optimized using a neural network architecture to maximize classification accuracy of the MNIST dataset while considering the blurring caused by the size of each prism. Simulated optical hardware performance for a range of prism sizes are reported.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

We investigate the feasibility of additively manufacturing optical components to accomplish task-specific classification in a computational imaging device. We report on the design, fabrication, and characterization of a non-traditional optical element that physically realizes an extremely compressed, optimized sensing matrix. The compression is achieved by designing an optical element that only samples the regions of object space most relevant to the classification algorithms, as determined by machine learning algorithms. The design process for the proposed optical element converts the optimal sensing matrix to a refractive surface composed of a minimized set of non-repeating, unique prisms. The optical elements are 3D printed using a Nanoscribe, which uses two-photon polymerization for high-precision printing. We describe the design of several computational imaging prototype elements. We characterize these components, including surface topography, surface roughness, and angle of prism facets of the as-fabricated elements.

We report on the design and fabrication of a computational imaging element used within a compressive task-specific imaging system. Fabrication via two-photon 3D printing is reported, as well as characterization of the fabricated element.

Nuisance and false alarms are prevalent in modern physical security systems and often overwhelm the alarm station operators. Deep learning has shown progress in detection and classification tasks, however, it has rarely been implemented as a solution to reduce the nuisance and false alarm rates in a physical security systems. Previous work has shown that transfer learning using a convolutional neural network can provide benefit to physical security systems by achieving high accuracy of physical security targets [10]. We leverage this work by coupling the convolutional neural network, which operates on a frame-by-frame basis, with temporal algorithms which evaluate a sequence of such frames (e.g. video analytics). We discuss several alternatives for performing this temporal analysis, in particular Long Short-Term Memory and Liquid State Machine, and demonstrate their respective value on exemplar physical security videos. We also outline an architecture for developing an ensemble learner which leverages the strength of each individual algorithm in its aggregation. The incorporation of these algorithms into physical security systems creates a new paradigm in which we aim to decrease the volume of nuisance and false alarms in order to allow the alarm station operators to focus on the most relevant threats.

Physical security systems (PSS) and humans are inescapably tied in the current physical security paradigm. Yet, physical security system evaluations often end at the console that displays information to the human. That is, these evaluations do not account for human-in-The-loop factors that can greatly impact performance of the security system, even though methods for doing so are well-established. This paper highlights two examples of methods for evaluating the human component of the current physical security system. One of these methods is qualitative, focusing on the information the human needs to adequately monitor alarms on a physical site. The other of these methods objectively measures the impact of false alarm rates on threat detection. These types of human-centric evaluations are often treated as unnecessary or not cost effective under the belief that human cognition is straightforward and errors can be either trained away or mitigated with technology. These assumptions are not always correct, are often surprising, and can often only be identified with objective assessments of human-system performance. Thus, taking the time to perform human element evaluations can identify unintuitive human-system weaknesses and can provide significant cost savings in the form of mitigating vulnerabilities and reducing costly system patches or retrofits to correct an issue after the system has been deployed.

Unmanned aircraft system (UAS) technologies have gained immense popularity in the commercial sector and have enabled capabilities that were not available just a short time ago. Once limited to the domain of highly skilled hobbyists or precision military instruments, consumer UAS are now widespread due to increased computational power, manufacturing techniques, and numerous commercial applications. The rise of consumer UAS and the low barrier to entry necessary to utilize these systems provides an increased potential for using a UAS as a delivery platform for malicious intent. This creates a new security concern which must be addressed. The contribution presented in this work is the realization of counter UAS security technology concepts viewed through the traditional security framework and the associated challenges to such a framework.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

The paper investigates the use of compressively sensed images in interactive image classification. To speed-up the classification process and avoid costly reconstruction, we consider the use of a feed-forward neural network in a reduced complexity image domain. The interactive image and video classification systems have been used for real-time demonstrations that have been effectively utilized in outreach activities for attracting middle-school students to STEM.

Advancements in machine learning (ML) and deep learning (DL) have enabled imaging systems to perform complex classification tasks, opening numerous problem domains to solutions driven by high quality imagers coupled with algorithmic elements. However, current ML and DL methods for target classification typically rely upon algorithms applied to data measured by traditional imagers. This design paradigm fails to enable the ML and DL algorithms to influence the sensing device itself, and treats the optimization of the sensor and algorithm as separate sequential elements. Additionally, this current paradigm narrowly investigates traditional images, and therefore traditional imaging hardware, as the primary means of data collection. We investigate alternative architectures for computational imaging systems optimized for specific classification tasks, such as digit classification. This involves a holistic approach to the design of the system from the imaging hardware to algorithms. Techniques to find optimal compressive representations of training data are discussed, and most-useful object-space information is evaluated. Methods to translate task-specific compressed data representations into non-traditional computational imaging hardware are described, followed by simulations of such imaging devices coupled with algorithmic classification using ML and DL techniques. Our approach allows for inexpensive, efficient sensing systems. Reduced storage and bandwidth are achievable as well since data representations are compressed measurements which is especially important for high data volume systems.

Deep learning techniques have demonstrated the ability to perform a variety of object recognition tasks using visible imager data; however, deep learning has not been implemented as a means to autonomously detect and assess targets of interest in a physical security system. We demonstrate the use of transfer learning on a convolutional neural network (CNN) to significantly reduce training time while keeping detection accuracy of physical security relevant targets high. Unlike many detection algorithms employed by video analytics within physical security systems, this method does not rely on temporal data to construct a background scene; targets of interest can halt motion indefinitely and still be detected by the implemented CNN. A key advantage of using deep learning is the ability for a network to improve over time. Periodic retraining can lead to better detection and higher confidence rates. We investigate training data size versus CNN test accuracy using physical security video data. Due to the large number of visible imagers, significant volume of data collected daily, and currently deployed human in the loop ground truth data, physical security systems present a unique environment that is well suited for analysis via CNNs. This could lead to the creation of algorithmic element that reduces human burden and decreases human analyzed nuisance alarms.

There is a desire to detect and assess unmanned aerial systems (UAS) with a high probability of detection and low nuisance alarm rates in numerous fields of security. Currently available solutions rely upon exploiting electronic signals emitted from the UAS. While these methods may enable some degree of security, they fail to address the emerging domain of autonomous UAS that do not transmit or receive information during the course of a mission. We examine frequency analysis of pixel fluctuation over time to exploit the temporal frequency signature present in imagery data of UAS. This signature is present for autonomous or controlled multirotor UAS and allows for lower pixels-on-target detection. The methodology also acts as a method of assessment due to the distinct frequency signatures of UAS when examined against the standard nuisance alarms such as birds or non-UAS electronic signal emitters. The temporal frequency analysis method is paired with machine learning algorithms to demonstrate a UAS detection and assessment method that requires minimal human interaction. The use of the machine learning algorithm allows each necessary human assess to increase the likelihood of autonomous assessment, allowing for increased system performance over time.

Physical unclonable functions (PUFs) are devices which are easily probed but difficult to predict. Optical PUFs have been discussed within the literature, with traditional optical PUFs typically using spatial light modulators, coherent illumination, and scattering volumes; however, these systems can be large, expensive, and difficult to maintain alignment in practical conditions. We propose and demonstrate a new kind of optical PUF based on computational imaging and compressive sensing to address these challenges with traditional optical PUFs. This work describes the design, simulation, and prototyping of this computational optical PUF (COPUF) that utilizes incoherent polychromatic illumination passing through an additively manufactured refracting optical polymer element. We demonstrate the ability to pass information through a COPUF using a variety of sampling methods, including the use of compressive sensing. The sensitivity of the COPUF system is also explored. We explore non-traditional PUF configurations enabled by the COPUF architecture. The double COPUF system, which employees two serially connected COPUFs, is proposed and analyzed as a means to authenticate and communicate between two entities that have previously agreed to communicate. This configuration enables estimation of a message inversion key without the calculation of individual COPUF inversion keys at any point in the PUF life cycle. Our results show that it is possible to construct inexpensive optical PUFs using computational imaging. This could lead to new uses of PUFs in places where electrical PUFs cannot be utilized effectively, as low cost tags and seals, and potentially as authenticating and communicating devices.

Abstract not provided.

Abstract not provided.

The evaluation of optical system performance in fog conditions typically requires field testing. This can be challenging due to the unpredictable nature of fog generation and the temporal and spatial nonuniformity of the phenomenon itself. We describe the Sandia National Laboratories fog chamber, a new test facility that enables the repeatable generation of fog within a 55m×3m×3m (L×W×H) environment, and demonstrate the fog chamber through a series of optical tests. These tests are performed to evaluate system image quality, determine meteorological optical range (MOR), and measure the number of particles in the atmosphere. Relationships between typical optical quality metrics, MOR values, and total number of fog particles are described using the data obtained from the fog chamber and repeated over a series of three tests.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

This report contains analysis of unmanned aerial systems as imaged by visible, short-wave infrared, mid-wave infrared, and long-wave infrared passive devices. Testing was conducted at the Nevada National Security Site (NNSS) during the week of August 15, 2016. Target images in all spectral bands are shown and contrast versus background is reported. Calculations are performed to determine estimated pixels-on-target for detection and assessment levels, and the number of pixels needed to cover a hemisphere for detection or assessment at defined distances. Background clutter challenges are qualitatively discussed for different spectral bands, and low contrast scenarios are highlighted for long-wave infrared imagers.

Lensless imaging systems have the potential to provide new capabilities for lower size and weight configuration than traditional imaging systems. Lensless imagers frequently utilize computational imaging techniques, which moves the complexity of the system away from optical subcomponents and into a calibration process whereby the measurement matrix is estimated. We report on the design, simulation, and prototyping of a lensless imaging system that utilizes a 3D printed optically transparent random scattering element. Development of end-to-end system simulations, which includes simulations of the calibration process, as well as the data processing algorithm used to generate an image from the raw data are presented. These simulations utilize GPU-based raytracing software, and parallelized minimization algorithms to bring complete system simulation times down to the order of seconds. Hardware prototype results are presented, and practical lessons such as the effect of sensor noise on reconstructed image quality are discussed. System performance metrics are proposed and evaluated to discuss image quality in a manner that is relatable to traditional image quality metrics. Various hardware instantiations are discussed.

Computational imagers fundamentally enable new optical hardware through the use of both physical and algorithmic elements. We report on the creation of a static lensless computational imaging system enabled by this paradigm.

We report on the design of a refracting prism array element for use in a computational lensless imaging system. The technique discussed enables creation of a refracting element that maximizes signal on a detector region.

The modeling and simulation of non-traditional imaging systems require holistic consideration of the end-to-end system. We demonstrate this approach through a tolerance analysis of a random scattering lensless imaging system.

Abstract not provided.

Three-dimensional (3D) information in a physical security system is a highly useful dis- criminator. The two-dimensional data from an imaging systems fails to provide target dis- tance and three-dimensional motion vector, which can be used to reduce nuisance alarm rates and increase system effectiveness. However, 3D imaging devices designed primarily for use in physical security systems are uncommon. This report discusses an architecture favorable to physical security systems; an inexpensive snapshot 3D imaging system utilizing a simple illumination system. The method of acquiring 3D data, tests to understand illumination de- sign, and software modifications possible to maximize information gathering capability are discussed.

The purpose of this document is to briefly frame the challenges of detecting low, slow, and small (LSS) unmanned aerial systems (UAS). The conclusion drawn from internal discussions and external reports is the following; detection of LSS UAS is a challenging problem that can- not be achieved with a single detection modality for all potential targets. Classification of LSS UAS, especially classification in the presence of background clutter (e.g., urban environment) or other non-threating targets (e.g., birds), is under-explored. Though information of avail- able technologies is sparse, many of the existing options for UAS detection appear to be in their infancy (when compared to more established ground-based air defense systems for larger and/or faster threats). Companies currently providing or developing technologies to combat the UAS safety and security problem are certainly worth investigating, however, no company has provided the statistical evidence necessary to support robust detection, identification, and/or neutralization of LSS UAS targets. The results of a market survey are included that highlights potential commercial entities that could contribute some technology that assists in the detection, classification, and neutral- ization of a LSS UAS. This survey found no clear and obvious commercial solution, though recommendations are given for further investigation of several potential systems.

The Johnson Criteria metric calculates probability of detection of an object imaged by an optical system, and was created in 1958 by John Johnson. As understanding of target detection has improved, detection models have evolved to better model additional factors such as weather, scene content, and object placement. The initial Johnson Criteria, while sufficient for technology and understanding at the time, does not accurately reflect current research into target acquisition and technology. Even though current research shows a dependence on human factors, there appears to be a lack of testing and modeling of human variability.

Numerous methods are available to measure the spatial frequency response (SFR) of an optical system. A recent change to the ISO 12233 photography resolution standard includes a sinusoidal Siemens star test target. We take the sinusoidal Siemens star proposed by the ISO 12233 standard, measure system SFR, and perform an analysis of errors induced by incorrectly identifying the center of a test target. We show a closed-form solution for the radial profile intensity measurement given an incorrectly determined center and describe how this error reduces the measured SFR of the system. Using the closed-form solution, we propose a two-step process by which test target centers are corrected and the measured SFR is restored to the nominal, correctly centered values.

Abstract not provided.

Abstract not provided.

The horizontal television lines (HTVL) metric has been the primary quantity used by division 6000 related to camera resolution for high consequence security systems. This document shows HTVL measurements are fundamen- tally insufficient as a metric to determine camera resolution, and propose a quantitative, standards based methodology by measuring the camera system modulation transfer function (MTF), the most common and accepted metric of res- olution in the optical science community. Because HTVL calculations are easily misinterpreted or poorly defined, we present several scenarios in which HTVL is frequently reported, and discuss their problems. The MTF metric is discussed, and scenarios are presented with calculations showing the application of such a metric.

Abstract not provided.

Abstract not provided.