6 papers
A Random Ensemble of Encrypted Vision Transformers for Adversarially Robust Defense
Ryota Iijima, Sayaka Shiota, Hitoshi Kiya
Deep neural networks (DNNs) are well known to be vulnerable to adversarial examples (AEs). In previous studies, the use of models encrypted with a secret key was demonstrated to be…
A Random Ensemble of Encrypted models for Enhancing Robustness against Adversarial Examples
Ryota Iijima, Sayaka Shiota, Hitoshi Kiya
Deep neural networks (DNNs) are well known to be vulnerable to adversarial examples (AEs). In addition, AEs have adversarial transferability, which means AEs generated for a source…
Block-Wise Encryption for Reliable Vision Transformer models
Hitoshi Kiya, Ryota Iijima, Teru Nagamori
This article presents block-wise image encryption for the vision transformer and its applications. Perceptual image encryption for deep learning enables us not only to protect the…
Enhanced Security against Adversarial Examples Using a Random Ensemble of Encrypted Vision Transformer Models
Ryota Iijima, Miki Tanaka, Sayaka Shiota +1
Deep neural networks (DNNs) are well known to be vulnerable to adversarial examples (AEs). In addition, AEs have adversarial transferability, which means AEs generated for a source…
An Access Control Method with Secret Key for Semantic Segmentation Models
Teru Nagamori, Ryota Iijima, Hitoshi Kiya
A novel method for access control with a secret key is proposed to protect models from unauthorized access in this paper. We focus on semantic segmentation models with the vision t…
An Encryption Method of ConvMixer Models without Performance Degradation
Ryota Iijima, Hitoshi Kiya
In this paper, we propose an encryption method for ConvMixer models with a secret key. Encryption methods for DNN models have been studied to achieve adversarial defense, model pro…