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Found 18 papers, 9 papers with code
Learning from Imbalanced Multiclass Sequential Data Streams Using Dynamically Weighted Conditional Random Fields
The present study introduces a method for improving the classification performance of imbalanced multiclass data streams from wireless body worn sensors.
Modeling Attack Resilient Reconfigurable Latent Obfuscation Technique for PUF based Lightweight Authentication
Physical unclonable functions (PUFs), as hardware security primitives, exploit manufacturing randomness to extract hardware instance-specific secrets.
Cryptography and Security
Real-Time Localization and Tracking of Multiple Radio-Tagged Animals with an Autonomous UAV
Autonomous aerial robots provide new possibilities to study the habitats and behaviors of endangered species through the efficient gathering of location information at temporal and spatial granularities not possible with traditional manual survey methods.
Systems and Control Robotics
Lightweight (Reverse) Fuzzy Extractor with Multiple Referenced PUF Responses
A Physical unclonable functions (PUF), alike a fingerprint, exploits manufacturing randomness to endow each physical item with a unique identifier.
Cryptography and Security
TrackerBots: Software in the Loop Study of Quad-Copter Robots for Locating Radio-tags in a 3D Space
We investigate the problem of tracking and planning for a UAV in a task to locate multiple radio-tagged wildlife in a three-dimensional (3D) setting in the context of our TrackerBots research project.
STRIP: A Defence Against Trojan Attacks on Deep Neural Networks
Since the trojan trigger is a secret guarded and exploited by the attacker, detecting such trojan inputs is a challenge, especially at run-time when models are in active operation.
Cryptography and Security
SparseSense: Human Activity Recognition from Highly Sparse Sensor Data-streams Using Set-based Neural Networks
Batteryless or so called passive wearables are providing new and innovative methods for human activity recognition (HAR), especially in healthcare applications for older people.
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems
Notably, in contrast to existing approaches, our approach removes the need for ground-truth labelled data or anomaly detection methods for Trojan detection or retraining a model or prior knowledge of an attack.
Cryptography and Security
Design and Evaluation of a Multi-Domain Trojan Detection Method on Deep Neural Networks
In particular, for vision tasks, we can always achieve a 0% FRR and FAR.
Cryptography and Security
Super Low Resolution RF Powered Accelerometers for Alerting on Hospitalized Patient Bed Exits
The sensor design allows deriving ultra low resolution acceleration data from the rate of change of unique RFID tag identifiers in accordance with the movement of a patient's upper body.
Attend And Discriminate: Beyond the State-of-the-Art for Human Activity Recognition using Wearable Sensors
Wearables are fundamental to improving our understanding of human activities, especially for an increasing number of healthcare applications from rehabilitation to fine-grained gait analysis.
Guided-GAN: Adversarial Representation Learning for Activity Recognition with Wearables
Human activity recognition (HAR) is an important research field in ubiquitous computing where the acquisition of large-scale labeled sensor data is tedious, labor-intensive and time consuming.
Generative Adversarial Network
Human Activity Recognition
+1
TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems
Now, an adversary can arm themselves with a patch that is naturalistic, less malicious-looking, physically realizable, highly effective achieving high attack success rates, and universal.
RamBoAttack: A Robust Query Efficient Deep Neural Network Decision Exploit
In our study, we first deep dive into recent state-of-the-art decision-based attacks in ICLR and SP to highlight the costly nature of discovering low distortion adversarial employing gradient estimation methods.
Query Efficient Decision Based Sparse Attacks Against Black-Box Deep Learning Models
The ability to extract information from solely the output of a machine learning model to craft adversarial perturbations to black-box models is a practical threat against real-world systems, such as autonomous cars or machine learning models exposed as a service (MLaaS).
Transferable Graph Backdoor Attack
In this paper, we disclose the TRAP attack, a Transferable GRAPh backdoor attack.
Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense
We recognize the adversarial learning approach for approximating the multi-modal posterior distribution of a Bayesian model can lead to mode collapse; consequently, the model's achievements in robustness and performance are sub-optimal.
BruSLeAttack: A Query-Efficient Score-Based Black-Box Sparse Adversarial Attack
We study the unique, less-well understood problem of generating sparse adversarial samples simply by observing the score-based replies to model queries.
