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CASCADE ADVERSARIAL MACHINE LEARNING REGULARIZED WITH A UNIFIED EMBEDDING
The dataset used in the paper is MNIST and CIFAR10. -
Negative Correlation Ensemble for Adversarial Examples Defense
The FashionMNIST and CIFAR-10 datasets are used to evaluate the performance of the Negative Correlation Ensemble (NCEn) defense strategy. -
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech...
This paper presents a well-known music identification method and implements it as a neural net. -
A Comprehensive Evaluation Framework for Deep Model Robustness
Deep neural networks (DNNs) have achieved remarkable performance across a wide range of applications, while they are vulnerable to adversarial examples, which motivates the... -
Manifold Adversarial Training
Manifold Adversarial Training (MAT) is a novel method to smooth the distributional manifold in the latent space. -
Frequency Centric Defense Mechanisms against Adversarial Examples
The proposed work uses the magnitude and phase of the Fourier Spectrum and the entropy of the image to defend against Adversarial Examples. -
MNIST and CIFAR-10
The MNIST dataset is a large dataset of handwritten digits, and the CIFAR-10 dataset is a dataset of images from 10 different classes. -
Semantic Equivalent Adversarial Data Augmentation for Visual Question Answering
Visual Question Answering (VQA) has achieved great success thanks to the fast development of deep neural networks (DNN). On the other hand, the data augmentation, as one of the... -
mini-ImageNet
The mini-ImageNet dataset is a subset of the ImageNet dataset, containing 60,000 images from 100 classes. -
DAFAR: Defending against Adversaries by Feedback-Autoencoder Reconstruction
Deep learning has shown impressive performance on challenging perceptual tasks and has been widely used in software to provide intelligent services. However, researchers found... -
MNIST and CIFAR-10 datasets
The MNIST and CIFAR-10 datasets are used to test the theory suggesting the existence of many saddle points in high-dimensional functions.
