4 papers · 1 filter
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…
A privacy-preserving method using secret key for convolutional neural network-based speech classification
Shoko Niwa, Sayaka Shiota, Hitoshi Kiya
In this paper, we propose a privacy-preserving method with a secret key for convolutional neural network (CNN)-based speech classification tasks. Recently, many methods related to…
Domain Adaptation for Efficiently Fine-tuning Vision Transformer with Encrypted Images
Teru Nagamori, Sayaka Shiota, Hitoshi Kiya
In recent years, deep neural networks (DNNs) trained with transformed data have been applied to various applications such as privacy-preserving learning, access control, and advers…
Hindering Adversarial Attacks with Multiple Encrypted Patch Embeddings
AprilPyone MaungMaung, Isao Echizen, Hitoshi Kiya
In this paper, we propose a new key-based defense focusing on both efficiency and robustness. Although the previous key-based defense seems effective in defending against adversari…