All your fake detector are belong to us: evaluating adversarial robustness of fake-news detectors under black-box settings
With the hyperconnectivity and ubiquity of the Internet, the fake news problem now presents
a greater threat than ever before. One promising solution for countering this threat is to …
a greater threat than ever before. One promising solution for countering this threat is to …
Physical adversarial attacks for camera-based smart systems: Current trends, categorization, applications, research challenges, and future outlook
Deep Neural Networks (DNNs) have shown impressive performance in computer vision
tasks; however, their vulnerability to adversarial attacks raises concerns regarding their …
tasks; however, their vulnerability to adversarial attacks raises concerns regarding their …
Qusecnets: Quantization-based defense mechanism for securing deep neural network against adversarial attacks
Adversarial examples have emerged as a significant threat to machine learning algorithms,
especially to the convolutional neural networks (CNNs). In this paper, we propose two …
especially to the convolutional neural networks (CNNs). In this paper, we propose two …
[HTML][HTML] Tamp-X: Attacking explainable natural language classifiers through tampered activations
While the technique of Deep Neural Networks (DNNs) has been instrumental in achieving
state-of-the-art results for various Natural Language Processing (NLP) tasks, recent works …
state-of-the-art results for various Natural Language Processing (NLP) tasks, recent works …
Building robust machine learning systems: Current progress, research challenges, and opportunities
Machine learning, in particular deep learning, is being used in almost all the aspects of life
to facilitate humans, specifically in mobile and Internet of Things (IoT)-based applications …
to facilitate humans, specifically in mobile and Internet of Things (IoT)-based applications …
Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally
Deep Neural Networks (DNNs) have been the driving force behind many of the recent
advances in machine learning. However, research has shown that DNNs are vulnerable to …
advances in machine learning. However, research has shown that DNNs are vulnerable to …
Fadec: A fast decision-based attack for adversarial machine learning
Due to the excessive use of cloud-based machine learning (ML) services, the smart cyber-
physical systems (CPS) are increasingly becoming vulnerable to black-box attacks on their …
physical systems (CPS) are increasingly becoming vulnerable to black-box attacks on their …
TrISec: training data-unaware imperceptible security attacks on deep neural networks
Most of the data manipulation attacks on deep neural networks (DNNs) during the training
stage introduce a perceptible noise that can be catered by preprocessing during inference …
stage introduce a perceptible noise that can be catered by preprocessing during inference …
Overview of security for smart cyber-physical systems
The tremendous growth of interconnectivity and dependencies of physical and cyber
domains in cyber-physical systems (CPS) makes them vulnerable to several security threats …
domains in cyber-physical systems (CPS) makes them vulnerable to several security threats …
Feshi: Feature map-based stealthy hardware intrinsic attack
Convolutional Neural Networks (CNN) have shown impressive performance in computer
vision, natural language processing, and many other applications, but they exhibit high …
vision, natural language processing, and many other applications, but they exhibit high …