  The usage of deep learning is being escalated in many applications. Due to
its outstanding performance, it is being used in a variety of security and
privacy-sensitive areas in addition to conventional applications. One of the
key aspects of deep learning efficacy is to have abundant data. This trait
leads to the usage of data which can be highly sensitive and private, which in
turn causes wariness with regard to deep learning in the general public.
Membership inference attacks are considered lethal as they can be used to
figure out whether a piece of data belongs to the training dataset or not. This
can be problematic with regards to leakage of training data information and its
characteristics. To highlight the significance of these types of attacks, we
propose an enhanced methodology for membership inference attacks based on
adversarial robustness, by adjusting the directions of adversarial
perturbations through label smoothing under a white-box setting. We evaluate
our proposed method on three datasets: Fashion-MNIST, CIFAR-10, and CIFAR-100.
Our experimental results reveal that the performance of our method surpasses
that of the existing adversarial robustness-based method when attacking
normally trained models. Additionally, through comparing our technique with the
state-of-the-art metric-based membership inference methods, our proposed method
also shows better performance when attacking adversarially trained models. The
code for reproducing the results of this work is available at
\url{https://github.com/plll4zzx/Evaluating-Membership-Inference-Through-Adversarial-Robustness}.
