United States

NAML 2022

Baselining Few-Shot Object Detection on Maritime Data

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

**Watch this video to hear about the AFCEA purpose.**

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NAML 2021

poster

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Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community


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</figure>

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop. The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community



In their strategic documents, the Commandant of the Marine Corps and the Chief of Naval Operations call for increased investment in artificial intelligence (AI) with the goal of achieving decision superiority. To responsibly deploy a deep learning model in a high stakes operating environment, we must know when these models are unsure or are providing information that is akin to an unsubstantiated guess. Deterministic deep learning models employed today provide no such indicator; Bayesian Deep Learning fills this gap and is a current AI research focal point. Bayesian deep learning models provide answers to classification and regression problems as well as associated uncertainty metrics. Moreover, these models perform on par with or outperform their deterministic counterparts. At the NPS Applied Artificial Intelligence Lab, research with Bayesian models is being conducted on several Navy-relevant problems using large scale datasets, achieving state of the art results. In this work, we show our results from three studies. In the first study, we focus on a single element passive sonar sensor and use more than a year of passive sonar data labeled through automated fusion with automatic identification system (AIS) information. Our Bayesian models classify multiple ships simultaneously (multi-label classification) with a weighted F1 score of 0.8 and provide the classification uncertainty. In the second study, we focus on using on-surface ship recordings to classify internal soundscapes including activation of onboard pumps. We demonstrate that a rise in the classification uncertainty correlates well with the transitions in the soundscape (transient sounds). In the third study, Bayesian models were used to predict temperature brightnesses from infrared images for ingestion by data assimilation models used by Fleet Numerical for improved weather forecasting. These models predict temperature brightness within 4 degrees on 95% of the test data. These results generalize to temporally independent datasets, an important indicator of potential performance in a live setting. Putting Bayesian deep learning models into Naval production-level systems would contribute to decision superiority in three ways. First, for problems where some amount of uncertainty is acceptable, such as sensor to sensor emulation, the level of uncertainty can be adjusted to control the quality of the emulation. Secondly, for high-risk problems, such as target classification, the uncertainty measures provided by deep Bayesian models can be used for real-time system failure detection (i.e., target misclassification). Lastly, these uncertainty measures allow for detection of a distribution shift due to sensor degradation or environmental changes, an indicator that a model needs to be retrained or that a different model is required. In all three cases, the problem of making a decision based on an unsubstantiated guess has been eliminated.

Bayesian Deep Learning for Navy Applications

We perform a study on the usefulness of cyclostationary features of digitally-modulated waveforms for signal classification. We conduct exploratory data analysis to examine the interactions between and the behavioral changes in higher order moments and cumulants across a range of waveform configurations. We apply ten supervised machine learning methods to measure model prediction accuracy on waveform modulation types based solely on cyclostationary features. In addition to evaluating the trade-off space of accuracy vs. computational efficiency of each classifier, we also explore four dimensionality reduction techniques for cluster analysis. Our empirical results suggest that cyclostationary features provide useful information for categorizing signals and clustering digitally-modulated waveforms. Lastly, we conclude with recommendations for future work.

How useful are Cyclostationary Features in Classifying Digitally-Modulated Waveforms

MITRE ATLAS is a knowledge base of adversary tactics, techniques, and case studies for machine learning systems based on real-world observations, demonstrations from machine learning red teams and security groups, and the state of the possible from academic research.

ATLAS enables researchers to navigate the landscape of threats to machine learning systems and is modeled after the MITRE ATT&CK® framework. The tactics and techniques in ATLAS are complementary to those in ATT&CK.

ATLAS can be used to:
- Develop case studies for your internal red teaming exercises
- Build defensive threat assessments from ATLAS TTPs
- Inform your security analysts of emerging threats to AI systems
- Report real-world adversary use of AI/ML techniques
- Understand adversary behaviors and mitigate threats to AI systems 

In this talk, we'll describe the ATLAS framework, give real-world examples of it's use, and give recommendations for based on our experience for future discovery of vulnerabilities in AI systems and related projects.

ATLAS (Adversarial Threat Landscape for AI Systems)

Deep Learning (DL) systems provide greater accuracy than earlier Machine Learning (ML) methods, but at the expense of needing large amounts of training data and being prone to overfitting, as well as a misclassification “brittleness” to particular noise patterns and certain natural object composites. These limitations make DL systems particularly vulnerable to cyber-attack, and unsuitable in their current form in cyber warfare. An architecture and respective approach is described in this paper that provides improved resiliency to DL systems, as well as accelerated learning for limited training data environments. This is accomplished through several composite techniques: matched pairing of DL classifiers with other AI/ML methods, separation or isolation layers using containers executing on a microkernel and mitigating misclassifications by using a cognitive reasoner that envelopes heterogeneous classifiers. A composite architecture incorporating these methods should provide the necessary resilience for cyber warfare environments.

A Resilient ML Architecture Approach

The Navy and DoD are prioritizing the rapid adoption of AI across domains in warfare to keep the technological advantage in the United States’ favor. Machine learning, the basis of much of the recent advancement in AI, suffers from a persistent and inadequately addressed critical flaw: adversarial examples. Since the initial discovery of adversarial examples in DNN classifiers in 2013, many new forms of these attacks have been invented and many narrow and particular defenses have been proposed. None of the defenses has survived counter testing. Some researchers have suggested that such susceptibility may be an unavoidable characteristic of problem formations in the space. At this time, no one effective, computationally acceptable, general approach has been discovered
which can shore up DNNs against this and related generalization problems. It is our contention that ML model robustness and resistance to the broad class of all ML adversarial examples can be improved with approaches that seek to train models to declare the data-point-sparse latent-space between data-dense regions of a model’s classification space as a barrier class. Identifying a general approach to training neural networks such that adversarial examples are either eliminated or computationally prohibitive representsa "holy grail" in the machine learning community. While we do not claim to have discovered this holy grail, we are collecting growing evidence that our intuitions hold valuable insights into the problem space which we believe will eventually bear significant fruit in the form of a viable general defense against all evasion adversarial attacks.

VALIDnet: A General Defense against Adversarial Examples in Neural Networks

Deep learning models are pervasive for a multitude of tasks, but the complexity of these models can limit interpretation and inhibit trust in their estimates of confidence. For the classification task, we investigate the induced geometric relationships between the class conditioned data distributions with the deep learning models’ output weight vectors. We propose a simple statistic to characterize the ’confidence’ of the model given a new input, which we call Angular Margin. We compare and contrast the statistic to respective Softmax outputs. We demonstrate that angular margin provides a simple yet superior statistic for detecting minimum-perturbation adversarial attacks and/or misclassified images than standard softmax predictions.

On Dogs and Cats and Adversarial Attacks

Current artificial intelligence (AI) systems excel in narrow scoped closed worlds such as board games and image classification. However, AI systems performance drops when accommodating to constantly changing conditions which are commonly faced by warfighters in the real world. To begin solving this limitation, open world novelty detection, characterization, and accommodation technology is needed to solve military relevant problems. We collaborate with DARPA SAIL-ON (Science of Artificial Intelligence and Learning for Open-world Novelty) performers using probabilistic programming, zero-shot plan updating, meta-model based adaptation, and knowledge graphs in multiple capstone projects to demonstrate the technology applicability.

DoD Applications of Novelty Accommodating Artificial Intelligence

We are applying a combined approach that utilizes generative adversarial networks (GANs) and transformers towards a general solution for data deconflation. Data deconflation expands on classical blind source separation problems, such as that of the Cocktail Party Problem, to superposition of data objects not confined to signals. Separating the conflated data objects (e.g aligning voices to their speakers, separating information from multiple co-located sensors, etc) into their individual sources allows for meaningful analysis of the data. Although classical solutions have proven successful in a certain problem set, it is unclear whether these solutions can be generalized. Our current approach works with a simple illustrative example for subsequence separation from a conflated sequence. The GAN’s objective is to be able to generate each subsequence with a label identifying its source, while being able to differentiate between properly and improperly generated sequences. We will present on our current status and the full network architecture and discuss application to more real-world examples. 

[1] Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017). Improved Training of Wasserstein GANs. arXiv [cs.LG]. Opgehaal van http://arxiv.org/abs/1704.00028 

[2] Hallman, R., & Cybenko, G. (2021). The Data Deconflation Problem: Moving from Classical to Emerging Solutions. In Proceedings of the 6th International Conference on Internet of Things, Big Data and Security. Special Session on Artificial Intelligence for Emerging IoT Systems: Open Challenges and Novel Perspectives. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0010530403750380 

[3] Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. arXiv [cs.CL]. Opgehaal van http://arxiv.org/abs/1409.3215

Machine Learning for Data Deconflation: Adopting GANs for Generalized Source Separation

Given a LiDAR point cloud structure, performing navigational tasks such as obstacle avoidance, can be computationally expensive and cumbersome due to massive nature of 3D point clouds. In this work, we leverage 2D Multi-agent Reinforcement Learning(MARL) and features extracted from the point cloud such as bullseye locations to aid our path planning process in a correspondence 2D floor plan. We change the environment dynamically with adversarial threats such as fire or leakage -- the RL agents can quickly find a new path given the learned environment. The method shows strong performances in both throughput and latency.

Navigation in 3D point cloud environment via multi-agent reinforcement learning in a correspondence 2D floorplan.

Automated spectrum spectrum management requires real-time feedback from sensors in the sensors in the networknetwork. This real-time feedback requires a large backhaul as the network scales. We employ machine learning classifiers at the edge to reduce the backhaul reduce the bandwidth requirements of the network.

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Baselining Few-Shot Object Detection on Maritime Data

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