Search Results for author:
Found 7 papers, 0 papers with code
Stealing the Invisible: Unveiling Pre-Trained CNN Models through Adversarial Examples and Timing Side-Channels
Machine learning, with its myriad applications, has become an integral component of numerous technological systems.
Resisting Adversarial Attacks in Deep Neural Networks using Diverse Decision Boundaries
The security of deep learning (DL) systems is an extremely important field of study as they are being deployed in several applications due to their ever-improving performance to solve challenging tasks.
On the Evaluation of User Privacy in Deep Neural Networks using Timing Side Channel
In this paper, as a separate case study, we demonstrate that a DL model secured with differential privacy (a popular countermeasure against MIA) is still vulnerable to MIA against an adversary exploiting Class Leakage.
PARL: Enhancing Diversity of Ensemble Networks to Resist Adversarial Attacks via Pairwise Adversarially Robust Loss Function
Ensemble methods against adversarial attacks demonstrate that an adversarial example is less likely to mislead multiple classifiers in an ensemble having diverse decision boundaries.
Deep-Lock: Secure Authorization for Deep Neural Networks
Trained Deep Neural Network (DNN) models are considered valuable Intellectual Properties (IP) in several business models.
How Secure are Deep Learning Algorithms from Side-Channel based Reverse Engineering?
Deep Learning algorithms have recently become the de-facto paradigm for various prediction problems, which include many privacy-preserving applications like online medical image analysis.
Adversarial Attacks and Defences: A Survey
Deep learning has emerged as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems which were difficult to solve using the traditional machine learning techniques in the past.
