5,416 research outputs found
Camouflage in predators
Camouflage – adaptations that prevent detection and/or recognition – is a key example of evolution by natural selection, making it a primary focus in evolutionary ecology and animal behaviour. Most work has focused on camouflage as an anti‐predator adaptation. However, predators also display specific colours, patterns and behaviours that reduce visual detection or recognition to facilitate predation. To date, very little attention has been given to predatory camouflage strategies. Although many of the same principles of camouflage studied in prey translate to predators, differences between the two groups (in motility, relative size, and control over the time and place of predation attempts) may alter selection pressures for certain visual and behavioural traits. This makes many predatory camouflage techniques unique and rarely documented. Recently, new technologies have emerged that provide a greater opportunity to carry out research on natural predator–prey interactions. Here we review work on the camouflage strategies used by pursuit and ambush predators to evade detection and recognition by prey, as well as looking at how work on prey camouflage can be applied to predators in order to understand how and why specific predatory camouflage strategies may have evolved. We highlight that a shift is needed in camouflage research focus, as this field has comparatively neglected camouflage in predators, and offer suggestions for future work that would help to improve our understanding of camouflage.Publisher PDFPeer reviewe
Post-Westgate SWAT : C4ISTAR Architectural Framework for Autonomous Network Integrated Multifaceted Warfighting Solutions Version 1.0 : A Peer-Reviewed Monograph
Police SWAT teams and Military Special Forces face mounting pressure and
challenges from adversaries that can only be resolved by way of ever more
sophisticated inputs into tactical operations. Lethal Autonomy provides
constrained military/security forces with a viable option, but only if
implementation has got proper empirically supported foundations. Autonomous
weapon systems can be designed and developed to conduct ground, air and naval
operations. This monograph offers some insights into the challenges of
developing legal, reliable and ethical forms of autonomous weapons, that
address the gap between Police or Law Enforcement and Military operations that
is growing exponentially small. National adversaries are today in many
instances hybrid threats, that manifest criminal and military traits, these
often require deployment of hybrid-capability autonomous weapons imbued with
the capability to taken on both Military and/or Security objectives. The
Westgate Terrorist Attack of 21st September 2013 in the Westlands suburb of
Nairobi, Kenya is a very clear manifestation of the hybrid combat scenario that
required military response and police investigations against a fighting cell of
the Somalia based globally networked Al Shabaab terrorist group.Comment: 52 pages, 6 Figures, over 40 references, reviewed by a reade
Physical Adversarial Attack meets Computer Vision: A Decade Survey
Although Deep Neural Networks (DNNs) have achieved impressive results in
computer vision, their exposed vulnerability to adversarial attacks remains a
serious concern. A series of works has shown that by adding elaborate
perturbations to images, DNNs could have catastrophic degradation in
performance metrics. And this phenomenon does not only exist in the digital
space but also in the physical space. Therefore, estimating the security of
these DNNs-based systems is critical for safely deploying them in the real
world, especially for security-critical applications, e.g., autonomous cars,
video surveillance, and medical diagnosis. In this paper, we focus on physical
adversarial attacks and provide a comprehensive survey of over 150 existing
papers. We first clarify the concept of the physical adversarial attack and
analyze its characteristics. Then, we define the adversarial medium, essential
to perform attacks in the physical world. Next, we present the physical
adversarial attack methods in task order: classification, detection, and
re-identification, and introduce their performance in solving the trilemma:
effectiveness, stealthiness, and robustness. In the end, we discuss the current
challenges and potential future directions.Comment: 32 pages. Under Revie
A Survey on Physical Adversarial Attack in Computer Vision
Over the past decade, deep learning has revolutionized conventional tasks
that rely on hand-craft feature extraction with its strong feature learning
capability, leading to substantial enhancements in traditional tasks. However,
deep neural networks (DNNs) have been demonstrated to be vulnerable to
adversarial examples crafted by malicious tiny noise, which is imperceptible to
human observers but can make DNNs output the wrong result. Existing adversarial
attacks can be categorized into digital and physical adversarial attacks. The
former is designed to pursue strong attack performance in lab environments
while hardly remaining effective when applied to the physical world. In
contrast, the latter focus on developing physical deployable attacks, thus
exhibiting more robustness in complex physical environmental conditions.
Recently, with the increasing deployment of the DNN-based system in the real
world, strengthening the robustness of these systems is an emergency, while
exploring physical adversarial attacks exhaustively is the precondition. To
this end, this paper reviews the evolution of physical adversarial attacks
against DNN-based computer vision tasks, expecting to provide beneficial
information for developing stronger physical adversarial attacks. Specifically,
we first proposed a taxonomy to categorize the current physical adversarial
attacks and grouped them. Then, we discuss the existing physical attacks and
focus on the technique for improving the robustness of physical attacks under
complex physical environmental conditions. Finally, we discuss the issues of
the current physical adversarial attacks to be solved and give promising
directions
Vision-based Testbeds For Control System Applicaitons
In the field of control systems, testbeds are a pivotal step in the validation and improvement of new algorithms for different applications. They provide a safe, controlled environment typically having a significantly lower cost of failure than the final application. Vision systems provide nonintrusive methods of measurement that can be easily implemented for various setups and applications. This work presents methods for modeling, removing distortion, calibrating, and rectifying single and two camera systems, as well as, two very different applications of vision-based control system testbeds: deflection control of shape memory polymers and trajectory planning for mobile robots. First, a testbed for the modeling and control of shape memory polymers (SMP) is designed. Red-green-blue (RGB) thresholding is used to assist in the webcam-based, 3D reconstruction of points of interest. A PID based controller is designed and shown to work with SMP samples, while state space models were identified from step input responses. Models were used to develop a linear quadratic regulator that is shown to work in simulation. Also, a simple to use graphical interface is designed for fast and simple testing of a series of samples. Second a robot testbed is designed to test new trajectory planning algorithms. A templatebased predictive search algorithm is investigated to process the images obtained through a lowcost webcam vision system, which is used to monitor the testbed environment. Also a userfriendly graphical interface is developed such that the functionalities of the webcam, robots, and optimizations are automated. The testbeds are used to demonstrate a wavefront-enhanced, Bspline augmented virtual motion camouflage algorithm for single or multiple robots to navigate through an obstacle dense and changing environment, while considering inter-vehicle conflicts, iv obstacle avoidance, nonlinear dynamics, and different constraints. In addition, it is expected that this testbed can be used to test different vehicle motion planning and control algorithms
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