Efficient Key-Based Adversarial Defense for ImageNet by Using
  Pre-trained Model
AprilPyone MaungMaung Isao Echizen Hitoshi Kiya
In this paper, we propose key-based defense model proliferation by leveraging
pre-trained models and utilizing recent efficient fine-tuning techniques on
ImageNet-1k classification. First, we stress that deploying key-based models on
edge devices is feasible with the latest model deployment advancements, such as
Apple CoreML, although the mainstream enterprise edge artificial intelligence
(Edge AI) has been focused on the Cloud. Then, we point out that the previous
key-based defense on on-device image classification is impractical for two
reasons: (1) training many classifiers from scratch is not feasible, and (2)
key-based defenses still need to be thoroughly tested on large datasets like
ImageNet. To this end, we propose to leverage pre-trained models and utilize
efficient fine-tuning techniques to proliferate key-based models even on
limited computing resources. Experiments were carried out on the ImageNet-1k
dataset using adaptive and non-adaptive attacks. The results show that our
proposed fine-tuned key-based models achieve a superior classification accuracy
(more than 10% increase) compared to the previous key-based models on
classifying clean and adversarial examples.