research
∙
05/23/2023
Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training
Although supervised learning has been highly successful in improving the...
share
research
∙
09/05/2022
A Principled Evaluation Protocol for Comparative Investigation of the Effectiveness of DNN Classification Models on Similar-but-non-identical Datasets
Deep Neural Network (DNN) models are increasingly evaluated using new re...
research
∙
05/31/2022
Exact Feature Collisions in Neural Networks
Predictions made by deep neural networks were shown to be highly sensiti...
research
∙
11/22/2021
Evaluating Adversarial Attacks on ImageNet: A Reality Check on Misclassification Classes
Although ImageNet was initially proposed as a dataset for performance be...
research
∙
06/14/2021
Selection of Source Images Heavily Influences the Effectiveness of Adversarial Attacks
Although the adoption rate of deep neural networks (DNNs) has tremendous...
research
∙
01/26/2021
Investigating the significance of adversarial attacks and their relation to interpretability for radar-based human activity recognition systems
Given their substantial success in addressing a wide range of computer v...
research
∙
07/07/2020
Regional Image Perturbation Reduces L_p Norms of Adversarial Examples While Maintaining Model-to-model Transferability
Regional adversarial attacks often rely on complicated methods for gener...
research
∙
06/02/2020
Perturbation Analysis of Gradient-based Adversarial Attacks
After the discovery of adversarial examples and their adverse effects on...
research
∙
07/30/2019
Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation
Deep learning models, which are increasingly being used in the field of ...
research
∙
07/30/2019
Not All Adversarial Examples Require a Complex Defense: Identifying Over-optimized Adversarial Examples with IQR-based Logit Thresholding
Detecting adversarial examples currently stands as one of the biggest ch...
research
∙
11/21/2018
