7 papers · 1 filter
Scene-Segmentation-Based Exposure Compensation for Tone Mapping of High Dynamic Range Scenes
Yuma Kinoshita, Hitoshi Kiya
We propose a novel scene-segmentation-based exposure compensation method for multi-exposure image fusion (MEF) based tone mapping. The aim of MEF-based tone mapping is to display h…
Disposable-key-based image encryption for collaborative learning of Vision Transformer
Rei Aso, Sayaka Shiota, Hitoshi Kiya
We propose a novel method for securely training the vision transformer (ViT) with sensitive data shared from multiple clients similar to privacy-preserving federated learning. In t…
Speech privacy-preserving methods using secret key for convolutional neural network models and their robustness evaluation
Shoko Niwa, Sayaka Shiota, Hitoshi Kiya
In this paper, we propose privacy-preserving methods with a secret key for convolutional neural network (CNN)-based models in speech processing tasks. In environments where untrust…
Fine-Tuning Text-To-Image Diffusion Models for Class-Wise Spurious Feature Generation
AprilPyone MaungMaung, Huy H. Nguyen, Hitoshi Kiya +1
We propose a method for generating spurious features by leveraging large-scale text-to-image diffusion models. Although the previous work detects spurious features in a large-scale…
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…
Efficient Fine-Tuning with Domain Adaptation for Privacy-Preserving Vision Transformer
Teru Nagamori, Sayaka Shiota, Hitoshi Kiya
We propose a novel method for privacy-preserving deep neural networks (DNNs) with the Vision Transformer (ViT). The method allows us not only to train models and test with visually…
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…