On enhancement of SEVIT for Chest XRay Classification using Defensive Distillation and Adversarial Training.
With the recent adaptation of deep learning in medical imaging problems, elevated vulnerabilities have been explored in reent CNN and ViT based solutions. The vulnerability of ViTs to adversarial, privacy, and confidentiality attacks raise serious concerns about their reliability in medical settings. This work aims to enhance the robustness of existing benchmark solution based on self-ensembling ViTs in tuberculosis chest X-ray classification problem. In the proposed work, we have presented a novel SEVIT-CNN architecture built over SEVIT, that utilizes the CNN modules for improved computational efficiency and robustness utilizing the adversarial training and defensive distillation. The proposed approach leverages the fact that adversarial training when performed with the combination of defensive distillation, presents significantly higher robustness against adversaries. CNN’s efficiency in learning spatial features through convolution operations at various levels of abstraction, along with training the model with adversarial examples improves its ability to handle perturbations and generalize better. By creating a distilled model with soft probabilities, uncertainty and variation are introduced into the output probabilities, making it more difficult for privacy attacks like model extraction. Extensive experiments performed with the proposed architecture on publicly available Tuberculosis X-ray dataset shows efficacy in terms of computational efficiency and enhanced robustness.
Ensembling · Adversarial Attack · Defensive Distillation