Search Results for author:
Found 16 papers, 0 papers with code
Fine-Tuning Text-To-Image Diffusion Models for Class-Wise Spurious Feature Generation
To this end, we utilize an existing approach of personalizing large-scale text-to-image diffusion models with available discovered spurious images and propose a new spurious feature similarity loss based on neural features of an adversarially robust model.
Stability Analysis of ChatGPT-based Sentiment Analysis in AI Quality Assurance
In the era of large AI models, the complex architecture and vast parameters present substantial challenges for effective AI quality management (AIQM), e. g. large language model (LLM).
Efficient Key-Based Adversarial Defense for ImageNet by Using Pre-trained Model
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.
Hindering Adversarial Attacks with Multiple Encrypted Patch Embeddings
In this paper, we propose a new key-based defense focusing on both efficiency and robustness.
Generative Model-Based Attack on Learnable Image Encryption for Privacy-Preserving Deep Learning
By taking advantage of leaked information from encrypted images, we propose a guided generative model as an attack on learnable image encryption to recover personally identifiable visual information.
Text-Guided Scene Sketch-to-Photo Synthesis
To this end, we leverage knowledge from recent large-scale pre-trained generative models, resulting in text-guided sketch-to-photo synthesis without the need for reference images.
Color-NeuraCrypt: Privacy-Preserving Color-Image Classification Using Extended Random Neural Networks
In recent years, with the development of cloud computing platforms, privacy-preserving methods for deep learning have become an urgent problem.
Access Control with Encrypted Feature Maps for Object Detection Models
In an experiment, the protected models allowed authorized users to obtain almost the same performance as that of non-protected models but also with robustness against unauthorized access without a key.
StyleGAN Encoder-Based Attack for Block Scrambled Face Images
In this paper, we propose an attack method to block scrambled face images, particularly Encryption-then-Compression (EtC) applied images by utilizing the existing powerful StyleGAN encoder and decoder for the first time.
Privacy-Preserving Image Classification Using ConvMixer with Adaptive Permutation Matrix
In this paper, we propose a privacy-preserving image classification method using encrypted images under the use of the ConvMixer structure.
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.
Privacy-Preserving Image Classification Using Vision Transformer
In this paper, we propose a privacy-preserving image classification method that is based on the combined use of encrypted images and the vision transformer (ViT).
Privacy-Preserving Image Classification Using Isotropic Network
In addition, compressible encrypted images, called encryption-then-compression (EtC) images, can be used for both training and testing without any adaptation network.
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.
Protection of SVM Model with Secret Key from Unauthorized Access
In this paper, we propose a block-wise image transformation method with a secret key for support vector machine (SVM) models.
A Protection Method of Trained CNN Model with Secret Key from Unauthorized Access
In this paper, we propose a novel method for protecting convolutional neural network (CNN) models with a secret key set so that unauthorized users without the correct key set cannot access trained models.
