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Found 12 papers, 0 papers with code
Image and Model Transformation with Secret Key for Vision Transformer
In this paper, we propose a combined use of transformed images and vision transformer (ViT) models transformed with a secret key.
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.
Access Control Using Spatially Invariant Permutation of Feature Maps for Semantic Segmentation Models
In an experiment, the protected models were demonstrated to allow rightful users to obtain almost the same performance as that of non-protected models but also to be robust against access by unauthorized users without a key.
A Protection Method of Trained CNN Model Using Feature Maps Transformed With Secret Key From Unauthorized Access
In this paper, we propose a model protection method for convolutional neural networks (CNNs) with a secret key so that authorized users get a high classification accuracy, and unauthorized users get a low classification accuracy.
Protecting Semantic Segmentation Models by Using Block-wise Image Encryption with Secret Key from Unauthorized Access
Since production-level trained deep neural networks (DNNs) are of a great business value, protecting such DNN models against copyright infringement and unauthorized access is in a rising demand.
Piracy-Resistant DNN Watermarking by Block-Wise Image Transformation with Secret Key
In this paper, we propose a novel DNN watermarking method that utilizes a learnable image transformation method with a secret key.
Transfer Learning-Based Model Protection With Secret Key
Models with pre-trained weights are fine-tuned by using such transformed images.
Ensemble of Models Trained by Key-based Transformed Images for Adversarially Robust Defense Against Black-box Attacks
In the proposed ensemble, a number of models are trained by using images transformed with different keys and block sizes, and then a voting ensemble is applied to the models.
Block-wise Image Transformation with Secret Key for Adversarially Robust Defense
In the best-case scenario, a model trained by using images transformed by FFX Encryption (block size of 4) yielded an accuracy of 92. 30% on clean images and 91. 48% under PGD attack with a noise distance of 8/255, which is close to the non-robust accuracy (95. 45%) for the CIFAR-10 dataset, and it yielded an accuracy of 72. 18% on clean images and 71. 43% under the same attack, which is also close to the standard accuracy (73. 70%) for the ImageNet dataset.
Training DNN Model with Secret Key for Model Protection
In this paper, we propose a model protection method by using block-wise pixel shuffling with a secret key as a preprocessing technique to input images for the first time.
Encryption Inspired Adversarial Defense for Visual Classification
The experiments are carried out on both adaptive and non-adaptive maximum-norm bounded white-box attacks while considering obfuscated gradients.
Adversarial Test on Learnable Image Encryption
Data for deep learning should be protected for privacy preserving.
