Zobrazeno 1 - 10
of 319
pro vyhledávání: '"Ranasinghe, Damith"'
Autonomous aerial vehicles can provide efficient and effective solutions for radio frequency (RF) source tracking and localizing problems with applications ranging from wildlife conservation to search and rescue operations. Existing lightweight, low-
Externí odkaz:
http://arxiv.org/abs/2410.13081
Autor:
Doan, Bao Gia, Nguyen, Dang Quang, Lindquist, Callum, Montague, Paul, Abraham, Tamas, De Vel, Olivier, Camtepe, Seyit, Kanhere, Salil S., Abbasnejad, Ehsan, Ranasinghe, Damith C.
Object detectors are vulnerable to backdoor attacks. In contrast to classifiers, detectors possess unique characteristics, architecturally and in task execution; often operating in challenging conditions, for instance, detecting traffic signs in auto
Externí odkaz:
http://arxiv.org/abs/2408.12122
Autor:
Doan, Bao Gia, Shamsi, Afshar, Guo, Xiao-Yu, Mohammadi, Arash, Alinejad-Rokny, Hamid, Sejdinovic, Dino, Ranasinghe, Damith C., Abbasnejad, Ehsan
Computational complexity of Bayesian learning is impeding its adoption in practical, large-scale tasks. Despite demonstrations of significant merits such as improved robustness and resilience to unseen or out-of-distribution inputs over their non- Ba
Externí odkaz:
http://arxiv.org/abs/2407.20891
Autonomous robots for gathering information on objects of interest has numerous real-world applications because of they improve efficiency, performance and safety. Realizing autonomy demands online planning algorithms to solve sequential decision mak
Externí odkaz:
http://arxiv.org/abs/2405.02605
We study the unique, less-well understood problem of generating sparse adversarial samples simply by observing the score-based replies to model queries. Sparse attacks aim to discover a minimum number-the l0 bounded-perturbations to model inputs to c
Externí odkaz:
http://arxiv.org/abs/2404.05311
Autor:
Doan, Bao Gia, Nguyen, Dang Quang, Montague, Paul, Abraham, Tamas, De Vel, Olivier, Camtepe, Seyit, Kanhere, Salil S., Abbasnejad, Ehsan, Ranasinghe, Damith C.
The vulnerability of machine learning-based malware detectors to adversarial attacks has prompted the need for robust solutions. Adversarial training is an effective method but is computationally expensive to scale up to large datasets and comes at t
Externí odkaz:
http://arxiv.org/abs/2403.18309
Autor:
Chen, Fei, Van Nguyen, Hoa, Leong, Alex S., Panicker, Sabita, Baker, Robin, Ranasinghe, Damith C.
Publikováno v:
Signal Processing (2024)
We consider the problem of tracking multiple, unknown, and time-varying numbers of objects using a distributed network of heterogeneous sensors. In an effort to derive a formulation for practical settings, we consider limited and unknown sensor field
Externí odkaz:
http://arxiv.org/abs/2401.00605
Autor:
Luo, Simon, Herrera, Adrian, Quirk, Paul, Chase, Michael, Ranasinghe, Damith C., Kanhere, Salil S.
Fuzzing is a highly-scalable software testing technique that uncovers bugs in a target program by executing it with mutated inputs. Over the life of a fuzzing campaign, the fuzzer accumulates inputs inducing new and interesting target behaviors, draw
Externí odkaz:
http://arxiv.org/abs/2312.04749
Autor:
Chen, Fei, Van Nguyen, Hoa, Taggart, David A., Falkner, Katrina, Rezatofighi, S. Hamid, Ranasinghe, Damith C.
Today, the most widespread, widely applicable technology for gathering data relies on experienced scientists armed with handheld radio telemetry equipment to locate low-power radio transmitters attached to wildlife from the ground. Although aerial ro
Externí odkaz:
http://arxiv.org/abs/2308.08104
Ability to test firmware on embedded devices is critical to discovering vulnerabilities prior to their adversarial exploitation. State-of-the-art automated testing methods rehost firmware in emulators and attempt to facilitate inputs from a diversity
Externí odkaz:
http://arxiv.org/abs/2308.07860