Search Results for author:
Found 16 papers, 8 papers with code
Model reference adaptive control for state and input constrained linear systems
Several modifications of the model reference adaptive control (MRAC) framework have been proposed to address input constraints in uncertain linear systems.
Adaptive identification of SISO linear infinite-dimensional systems
The unknown parameter can be reconstructed using the transfer function coefficient estimates obtained with n large and the algebraic expressions relating the transfer function coefficients to the unknown parameter.
Distribution-aware Fairness Test Generation
This work addresses how to validate group fairness in image recognition software.
Repairing Adversarial Texts through Perturbation
Furthermore, such attacks are impossible to eliminate, i. e., the adversarial perturbation is still possible after applying mitigation methods such as adversarial training.
AequeVox: Automated Fairness Testing of Speech Recognition Systems
AequeVox simulates different environments to assess the effectiveness of ASR systems for different populations.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems
The threats faced by cyber-physical systems (CPSs) in critical infrastructure have motivated research into a multitude of attack detection mechanisms, including anomaly detectors based on neural network models.
SCOPE: Secure Compiling of PLCs in Cyber-Physical Systems
We empirically measure the computational overhead caused by our approach on two experimental settings based on real CPS.
Cryptography and Security
Astraea: Grammar-based Fairness Testing
We propose a grammar-based fairness testing approach (called ASTRAEA) which leverages context-free grammars to generate discriminatory inputs that reveal fairness violations in software systems.
Towards Backdoor Attacks and Defense in Robust Machine Learning Models
Specifically, given a robust Deep Neural Network (DNN) that is trained using PGD-based first-order adversarial training approach, AEGIS uses feature clustering to effectively detect whether such DNNs are backdoor-infected or clean.
Callisto: Entropy based test generation and data quality assessment for Machine Learning Systems
We conduct and present an extensive user study to validate the results of CALLISTO on identifying low quality data from four state-of-the-art real world datasets.
KLEESPECTRE: Detecting Information Leakage through Speculative Cache Attacks via Symbolic Execution
Spectre attacks disclosed in early 2018 expose data leakage scenarios via cache side channels.
Cryptography and Security
Model Agnostic Defence against Backdoor Attacks in Machine Learning
In this work, we present NEO, a model agnostic framework to detect and mitigate such backdoor attacks in image classification ML models.
Road Context-aware Intrusion Detection System for Autonomous Cars
With such ongoing road context, RAIDS validates corresponding frames observed on the in-vehicle network.
Grammar Based Directed Testing of Machine Learning Systems
The massive progress of machine learning has seen its application over a variety of domains in the past decade.
oo7: Low-overhead Defense against Spectre Attacks via Program Analysis
In this paper, we propose oo7, a static analysis approach that can mitigate Spectre attacks by detecting potentially vulnerable code snippets in program binaries and protecting them against the attack by patching them.
Cryptography and Security
Automated Directed Fairness Testing
We show that AEQUITAS effectively generates inputs to uncover fairness violation in all the subject classifiers and systematically improves the fairness of the respective models using the generated test inputs.
