Adversarial Fine-tuning of Compressed Neural Networks for Joint Improvement of Robustness and Efficiency

arXiv:2403.09441v1 Announce Type: new
Abstract: As deep learning (DL) models are increasingly being integrated into our everyday lives, ensuring their safety by making them robust against adversarial attacks has become increasingly critical. DL models have been found to be susceptible to adversarial attacks which can be achieved by introducing small, targeted perturbations to disrupt the input data. Adversarial training has been presented as a mitigation strategy which can result in more robust models. This adversarial robustness comes with additional computational …

abstract adversarial adversarial attacks arxiv attacks become cs.lg deep learning efficiency fine-tuning found improvement making networks neural networks robust robustness safety them type