Deep learning-based anomaly detection in cyber-physical systems: Progress and opportunities
Anomaly detection is crucial to ensure the security of cyber-physical systems (CPS).
However, due to the increasing complexity of CPSs and more sophisticated attacks …
However, due to the increasing complexity of CPSs and more sophisticated attacks …
A review of explainable deep learning cancer detection models in medical imaging
Deep learning has demonstrated remarkable accuracy analyzing images for cancer
detection tasks in recent years. The accuracy that has been achieved rivals radiologists and …
detection tasks in recent years. The accuracy that has been achieved rivals radiologists and …
Trustworthy ai: A computational perspective
In the past few decades, artificial intelligence (AI) technology has experienced swift
developments, changing everyone's daily life and profoundly altering the course of human …
developments, changing everyone's daily life and profoundly altering the course of human …
Invisible for both camera and lidar: Security of multi-sensor fusion based perception in autonomous driving under physical-world attacks
In Autonomous Driving (AD) systems, perception is both security and safety critical. Despite
various prior studies on its security issues, all of them only consider attacks on camera-or …
various prior studies on its security issues, all of them only consider attacks on camera-or …
On the (in) fidelity and sensitivity of explanations
We consider objective evaluation measures of saliency explanations for complex black-box
machine learning models. We propose simple robust variants of two notions that have been …
machine learning models. We propose simple robust variants of two notions that have been …
Februus: Input purification defense against trojan attacks on deep neural network systems
We propose Februus; a new idea to neutralize highly potent and insidious Trojan attacks on
Deep Neural Network (DNN) systems at run-time. In Trojan attacks, an adversary activates a …
Deep Neural Network (DNN) systems at run-time. In Trojan attacks, an adversary activates a …
“real attackers don't compute gradients”: bridging the gap between adversarial ml research and practice
Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …
{CADE}: Detecting and explaining concept drift samples for security applications
Concept drift poses a critical challenge to deploy machine learning models to solve practical
security problems. Due to the dynamic behavior changes of attackers (and/or the benign …
security problems. Due to the dynamic behavior changes of attackers (and/or the benign …
A survey of data-driven and knowledge-aware explainable ai
We are witnessing a fast development of Artificial Intelligence (AI), but it becomes
dramatically challenging to explain AI models in the past decade.“Explanation” has a flexible …
dramatically challenging to explain AI models in the past decade.“Explanation” has a flexible …
Does physical adversarial example really matter to autonomous driving? towards system-level effect of adversarial object evasion attack
In autonomous driving (AD), accurate perception is indispensable to achieving safe and
secure driving. Due to its safety-criticality, the security of AD perception has been widely …
secure driving. Due to its safety-criticality, the security of AD perception has been widely …