TY - JOUR
AU1 - Tuna, Omer Faruk
AU2 - Catak, Ferhat Ozgur
AU3 - Eskil, M. Taner
AB - Deep neural network (DNN) models are widely renowned for their resistance to random perturbations. However, researchers have found out that these models are indeed extremely vulnerable to deliberately crafted and seemingly imperceptible perturbations of the input, referred to as adversarial examples. Adversarial attacks have the potential to substantially compromise the security of DNN-powered systems and posing high risks especially in the areas where security is a top priority. Numerous studies have been conducted in recent years to defend against these attacks and to develop more robust architectures resistant to adversarial threats. In this study, we propose a new architecture and enhance a recently proposed technique by which we can restore adversarial samples back to their original class manifold. We leverage the use of several uncertainty metrics obtained from Monte Carlo dropout (MC Dropout) estimates of the model together with the model’s own loss function and combine them with the use of defensive distillation technique to defend against these attacks. We have experimentally evaluated and verified the efficacy of our approach on MNIST (Digit), MNIST (Fashion) and CIFAR10 datasets. In our experiments, we showed that our proposed method reduces the attack’s success rate lower than 5% without compromising clean accuracy.
TI - TENET: a new hybrid network architecture for adversarial defense
JF - International Journal of Information Security
DO - 10.1007/s10207-023-00675-1
DA - 2023-08-01
UR - https://www.deepdyve.com/lp/springer-journals/tenet-a-new-hybrid-network-architecture-for-adversarial-defense-i4wJVFo0bn
SP - 987
EP - 1004
VL - 22
IS - 4
DP - DeepDyve
ER -