Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …

As the machine learning-related technology has made great progress in recent years, deep
neural networks are widely used in many scenarios, including security-critical ones, which …

With the increasing proliferation of hardware accelerators and the predicted continued
increase in the heterogeneity of future computing systems, it is necessary to understand the …

Bit-flip attacks (BFAs) have attracted substantial attention recently, in which an adversary
could tamper with a small number of model parameter bits to break the integrity of DNNs. To …

Reproducibility is crucial to the advancement of science; it strengthens confidence in
seemingly contradictory results and expands the boundaries of known discoveries …

Bit-Flip Attacks (BFAs) involve adversaries manipulating a model's parameter bits to
undermine its accuracy significantly. They typically target the most vulnerable parameters …

As a type of valuable intellectual property (IP), deep neural network (DNN) models have
been protected by techniques like watermarking. However, such passive model protection …

Deep neural networks (DNNs) are widely deployed on real-world devices. Concerns
regarding their security have gained great attention from researchers. Recently, a new …

As Field-programmable gate arrays (FPGAs) are widely adopted in clouds to accelerate
Deep Neural Networks (DNN), such virtualization environments have posed many new …

Field-programmable gate arrays (FPGAs) are becoming widely used accelerators for a
myriad of datacenter applications due to their flexibility and energy efficiency. Among these …