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FashionMNIST and CIFAR-10
The dataset used in the paper is FashionMNIST and CIFAR-10, which are commonly used datasets for image classification tasks. -
ImageNet Noise
The dataset used in the paper is the ImageNet noise dataset, which contains 60,000 32x32 color images with random labels. -
Oxford Pets
The dataset used in the paper is a collection of trained networks and their corresponding datasets. -
CIFAR-10, CIFAR-100, and Tiny-Imagenet
The dataset used in the paper is CIFAR-10, CIFAR-100, and Tiny-Imagenet. -
ImageNet-2k
The dataset used in the paper is the ImageNet-2k dataset, a large dataset for image classification. -
ImageNet and YouTube-8M
The dataset used in this paper is not explicitly described. However, it is mentioned that the authors used datasets such as ImageNet and YouTube-8M. -
ImageNet 642
The dataset used in the paper is ImageNet 642, a large-scale image classification dataset. -
ImageNet 322
The dataset used in the paper is ImageNet 322, a large-scale image classification dataset. -
Dispersed Pixel Perturbation-based Imperceptible
Typical deep neural network (DNN) backdoor at- -
CASIA ITDE v.2
The CASIA ITDE v.2 dataset is a binary classification dataset that differentiates between authentic and tampered images. -
MNIST, CIFAR10 and STL10
The dataset used in the paper is MNIST, CIFAR10 and STL10. These are datasets for image classification tasks. -
ResNet-50 dataset
The dataset used in this paper is the ResNet-50 dataset. -
ISLVRC2012
The dataset used in the paper is ISLVRC2012, a dataset for image classification. -
Very Deep Convolutional Networks for Large-Scale Image Recognition
The dataset consists of 60,000 images of objects in 200 categories, with 300 images per category. -
CIFAR-10, CIFAR-100, and SVHN datasets
The dataset used in the paper is the CIFAR-10 and CIFAR-100 datasets, and the SVHN dataset. -
Oxford Flowers
The dataset used in the paper is a collection of trained networks and their corresponding datasets.
