  Privacy and confidentiality of medical data are of utmost importance in
healthcare settings. ViTs, the SOTA vision model, rely on large amounts of
patient data for training, which raises concerns about data security and the
potential for unauthorized access. Adversaries may exploit vulnerabilities in
ViTs to extract sensitive patient information and compromising patient privacy.
This work address these vulnerabilities to ensure the trustworthiness and
reliability of ViTs in medical applications. In this work, we introduced a
defensive diffusion technique as an adversarial purifier to eliminate
adversarial noise introduced by attackers in the original image. By utilizing
the denoising capabilities of the diffusion model, we employ a reverse
diffusion process to effectively eliminate the adversarial noise from the
attack sample, resulting in a cleaner image that is then fed into the ViT
blocks. Our findings demonstrate the effectiveness of the diffusion model in
eliminating attack-agnostic adversarial noise from images. Additionally, we
propose combining knowledge distillation with our framework to obtain a
lightweight student model that is both computationally efficient and robust
against gray box attacks. Comparison of our method with a SOTA baseline method,
SEViT, shows that our work is able to outperform the baseline. Extensive
experiments conducted on a publicly available Tuberculosis X-ray dataset
validate the computational efficiency and improved robustness achieved by our
proposed architecture.
