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Found 17 papers, 1 papers with code
A Random Ensemble of Encrypted Vision Transformers for Adversarially Robust Defense
Deep neural networks (DNNs) are well known to be vulnerable to adversarial examples (AEs).
Efficient Fine-Tuning with Domain Adaptation for Privacy-Preserving Vision Transformer
We propose a novel method for privacy-preserving deep neural networks (DNNs) with the Vision Transformer (ViT).
A Random Ensemble of Encrypted models for Enhancing Robustness against Adversarial Examples
In previous studies, it was confirmed that the vision transformer (ViT) is more robust against the property of adversarial transferability than convolutional neural network (CNN) models such as ConvMixer, and moreover encrypted ViT is more robust than ViT without any encryption.
A privacy-preserving method using secret key for convolutional neural network-based speech classification
To promote research on privacy preservation for speech classification, we provide an encryption method with a secret key in CNN-based speech classification systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Domain Adaptation for Efficiently Fine-tuning Vision Transformer with Encrypted Images
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 adversarial defenses.
Enhanced Security against Adversarial Examples Using a Random Ensemble of Encrypted Vision Transformer Models
In previous studies, it was confirmed that the vision transformer (ViT) is more robust against the property of adversarial transferability than convolutional neural network (CNN) models such as ConvMixer, and moreover encrypted ViT is more robust than ViT without any encryption.
Access Control of Semantic Segmentation Models Using Encrypted Feature Maps
In this paper, we propose an access control method with a secret key for semantic segmentation models for the first time so that unauthorized users without a secret key cannot benefit from the performance of trained models.
Adversarial Detector with Robust Classifier
Deep neural network (DNN) models are wellknown to easily misclassify prediction results by using input images with small perturbations, called adversarial examples.
An Overview of Compressible and Learnable Image Transformation with Secret Key and Its Applications
In this paper, we focus on a class of image transformation referred to as learnable image encryption, which is applicable to privacy-preserving machine learning and adversarially robust defense.
A universal detector of CNN-generated images using properties of checkerboard artifacts in the frequency domain
In addition, an ensemble of the proposed detector with emphasized spectrums and a conventional detector is proposed to improve the performance of these methods.
An Image Fusion Scheme for Single-Shot High Dynamic Range Imaging with Spatially Varying Exposures
This paper proposes a novel multi-exposure image fusion (MEF) scheme for single-shot high dynamic range imaging with spatially varying exposures (SVE).
Single-Shot High Dynamic Range Imaging with Spatially Varying Exposures Considering Hue Distortion
We proposes a novel single-shot high dynamic range imaging scheme with spatially varying exposures (SVE) considering hue distortion.
A Retinex-based Image Enhancement Scheme with Noise Aware Shadow-up Function
This paper proposes a novel image contrast enhancement method based on both a noise aware shadow-up function and Retinex (retina and cortex) decomposition.
A Pseudo Multi-Exposure Fusion Method Using Single Image
The proposed method enables us to produce pseudo multi-exposure images from a single image.
Multi-Exposure Image Fusion Based on Exposure Compensation
This paper proposes a novel multi-exposure image fusion method based on exposure compensation.
Super-Resolution using Convolutional Neural Networks without Any Checkerboard Artifacts
It is well-known that a number of excellent super-resolution (SR) methods using convolutional neural networks (CNNs) generate checkerboard artifacts.
Automatic Exposure Compensation for Multi-Exposure Image Fusion
In conventional works, it has been pointed out that the quality of those multi-exposure images can be improved by adjusting the luminance of them.
