Towards the Transferable Audio Adversarial Attack via Ensemble Methods. (arXiv:2304.08811v1 [cs.CR])

In recent years, deep learning (DL) models have achieved significant progress
in many domains, such as autonomous driving, facial recognition, and speech
recognition. However, the vulnerability of deep learning models to adversarial
attacks has raised serious concerns in the community because of their
insufficient robustness and generalization. Also, transferable attacks have
become a prominent method for black-box attacks. In this work, we explore the
potential factors that impact adversarial examples (AEs) transferability in
DL-based speech recognition. We also discuss the …

adversarial adversarial attacks aes attack attacks audio autonomous autonomous driving box community deep learning discuss domains driving facial facial recognition impact nature progress recognition robustness serious speech systems vulnerability work