Learning adversarially robust models require invariant predictions to a small neighborhood
of its natural inputs, often encountering insufficient model capacity. There is research …

Deep neural network (DNN) models have been widely used in many tasks due to their
superior performance. However, DNN models are usually vulnerable to adversarial example …

Federated learning (FL) has emerged as a prominent paradigm enabling collaborative
model training without transmitting local data, thereby safeguarding data privacy. However …

Deep neural networks for classification are vulnerable to adversarial attacks, where small
perturbations to input samples lead to incorrect predictions. This susceptibility, combined …

In the evasion attacks against deep neural networks (DNN), the attacker generates
adversarial instances that are visually indistinguishable from benign samples and sends …

Deep learning has achieved great results in the last decade, however, it is sensitive to so
called adversarial attacks-small perturbations of the input that cause the network to classify …

Despite the increasing attack ability and transferability of adversarial examples (AE), their
security, ie, how unlikely they can be detected, has been ignored more or less. Without the …

Detecting adversarial samples is crucial for maintaining the security of automatic modulation
recognition (AMR) systems, as adversarial attacks could severely compromise wireless …

Neural networks are fragile because adversarial examples can readily assault them. As a
result of the current scenario, academics from various countries have paid close attention to …

The anti-detection adversarial attack is an evolutionary attack. It can both fool a CNN model
to give error classification outputs and evade some detection-based defenses. In this paper …