436 research outputs found
A framework based on Gaussian mixture models and Kalman filters for the segmentation and tracking of anomalous events in shipboard video
Anomalous indications in monitoring equipment on board U.S. Navy vessels must be handled in a timely manner to prevent catastrophic system failure. The development of sensor data analysis techniques to assist a ship\u27s crew in monitoring machinery and summon required ship-to-shore assistance is of considerable benefit to the Navy. In addition, the Navy has a large interest in the development of distance support technology in its ongoing efforts to reduce manning on ships. In this thesis, algorithms have been developed for the detection of anomalous events that can be identified from the analysis of monochromatic stationary ship surveillance video streams. The specific anomalies that we have focused on are the presence and growth of smoke and fire events inside the frames of the video stream. The algorithm consists of the following steps. First, a foreground segmentation algorithm based on adaptive Gaussian mixture models is employed to detect the presence of motion in a scene. The algorithm is adapted to emphasize gray-level characteristics related to smoke and fire events in the frame. Next, shape discriminant features in the foreground are enhanced using morphological operations. Following this step, the anomalous indication is tracked between frames using Kalman filtering. Finally, gray level shape and motion features corresponding to the anomaly are subjected to principal component analysis and classified using a multilayer perceptron neural network. The algorithm is exercised on 68 video streams that include the presence of anomalous events (such as fire and smoke) and benign/nuisance events (such as humans walking the field of view). Initial results show that the algorithm is successful in detecting anomalies in video streams, and is suitable for application in shipboard environments
SINGULAB - A Graphical user Interface for the Singularity Analysis of Parallel Robots based on Grassmann-Cayley Algebra
This paper presents SinguLab, a graphical user interface for the singularity
analysis of parallel robots. The algorithm is based on Grassmann-Cayley
algebra. The proposed tool is interactive and introduces the designer to the
singularity analysis performed by this method, showing all the stages along the
procedure and eventually showing the solution algebraically and graphically,
allowing as well the singularity verification of different robot poses.Comment: Advances in Robot Kinematics, Batz sur Mer : France (2008
SoK: Anti-Facial Recognition Technology
The rapid adoption of facial recognition (FR) technology by both government
and commercial entities in recent years has raised concerns about civil
liberties and privacy. In response, a broad suite of so-called "anti-facial
recognition" (AFR) tools has been developed to help users avoid unwanted facial
recognition. The set of AFR tools proposed in the last few years is
wide-ranging and rapidly evolving, necessitating a step back to consider the
broader design space of AFR systems and long-term challenges. This paper aims
to fill that gap and provides the first comprehensive analysis of the AFR
research landscape. Using the operational stages of FR systems as a starting
point, we create a systematic framework for analyzing the benefits and
tradeoffs of different AFR approaches. We then consider both technical and
social challenges facing AFR tools and propose directions for future research
in this field.Comment: Camera-ready version for Oakland S&P 202
Blacklight: Defending Black-Box Adversarial Attacks on Deep Neural Networks
The vulnerability of deep neural networks (DNNs) to adversarial examples is
well documented. Under the strong white-box threat model, where attackers have
full access to DNN internals, recent work has produced continual advancements
in defenses, often followed by more powerful attacks that break them.
Meanwhile, research on the more realistic black-box threat model has focused
almost entirely on reducing the query-cost of attacks, making them increasingly
practical for ML models already deployed today.
This paper proposes and evaluates Blacklight, a new defense against black-box
adversarial attacks. Blacklight targets a key property of black-box attacks: to
compute adversarial examples, they produce sequences of highly similar images
while trying to minimize the distance from some initial benign input. To detect
an attack, Blacklight computes for each query image a compact set of one-way
hash values that form a probabilistic fingerprint. Variants of an image produce
nearly identical fingerprints, and fingerprint generation is robust against
manipulation. We evaluate Blacklight on 5 state-of-the-art black-box attacks,
across a variety of models and classification tasks. While the most efficient
attacks take thousands or tens of thousands of queries to complete, Blacklight
identifies them all, often after only a handful of queries. Blacklight is also
robust against several powerful countermeasures, including an optimal black-box
attack that approximates white-box attacks in efficiency. Finally, Blacklight
significantly outperforms the only known alternative in both detection coverage
of attack queries and resistance against persistent attackers
GLAZE: Protecting Artists from Style Mimicry by Text-to-Image Models
Recent text-to-image diffusion models such as MidJourney and Stable Diffusion
threaten to displace many in the professional artist community. In particular,
models can learn to mimic the artistic style of specific artists after
"fine-tuning" on samples of their art. In this paper, we describe the design,
implementation and evaluation of Glaze, a tool that enables artists to apply
"style cloaks" to their art before sharing online. These cloaks apply barely
perceptible perturbations to images, and when used as training data, mislead
generative models that try to mimic a specific artist. In coordination with the
professional artist community, we deploy user studies to more than 1000
artists, assessing their views of AI art, as well as the efficacy of our tool,
its usability and tolerability of perturbations, and robustness across
different scenarios and against adaptive countermeasures. Both surveyed artists
and empirical CLIP-based scores show that even at low perturbation levels
(p=0.05), Glaze is highly successful at disrupting mimicry under normal
conditions (>92%) and against adaptive countermeasures (>85%)
Management strategies to minimize the dredging impacts of coastal development on fish and fisheries
Accelerating coastal development and shipping activities dictate that dredging operations will intensify, increasing potential impacts to fishes. Coastal fishes have high economic, ecological, and conservation significance and there is a need for evidencebased, quantitative guidelines on how to mitigate the impacts of dredging activities. We assess the potential risk from dredging to coastal fish and fisheries on a global scale.We then develop quantitative guidelines for two management strategies: threshold reference values and seasonal restrictions. Globally, threatened species and nearshore fisheries occur within close proximity to ports. We find that maintaining suspended sediment concentrations below 44 mg/L (15–121 bootstrapped CI) and for less than 24 hours would protect 95% of fishes from dredging-induced mortality. Implementation of seasonal restrictions during peak periods of reproduction and recruitment could further protect species from dredging impacts. This study details the first evidence-based defensible approach to minimize impacts to coastal fishes from dredging activities
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