Recently, many applications have begun to employ deep neural networks (DNN), such as
image recognition and safety-critical applications, for more accurate results. One of the most …

This work presents a comprehensive exploration of Reverse Engineering of Deceptions
(RED) in the field of adversarial machine learning. It delves into the intricacies of machine …

Adversarial attacks have been demonstrated to fool the deep classification networks. There
are two key characteristics of these attacks: firstly, these perturbations are mostly additive …

Deep neural networks (DNNs) are well known to be vulnerable to adversarial examples
(AEs). In previous studies, the use of models encrypted with a secret key was demonstrated …

The vulnerability of deep neural networks (DNNs) to adversarial attacks has attracted
attention in many fields, and researchers have sought methods to improve the robustness of …

Abstract We introduce the Lossy Implicit Network Activation Coding (LINAC) defence, an
input transformation which successfully hinders several common adversarial attacks on …

Deep neural network-based classifiers have been shown to be vulnerable to imperceptible
perturbations to their input, such as $\ell_p $-bounded norm adversarial attacks. This has …

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples/attacks,
raising concerns about their reliability in safety-critical applications. A number of defense …

In this paper, we propose a new key-based defense focusing on both efficiency and
robustness. Although the previous key-based defense seems effective in defending against …

Artificial intelligence technologies are becoming increasingly prevalent in resource-
constrained, safety-critical embedded systems. Numerous methods exist to enhance the …