Combatting Adversarial Attacks through Denoising and Dimensionality Reduction: A Cascaded Autoencoder Approach

7 December 2018

Papers citing "Combatting Adversarial Attacks through Denoising and Dimensionality Reduction: A Cascaded Autoencoder Approach"

9 / 9 papers shown

Title
Understanding Deep Learning defenses Against Adversarial Examples Through Visualizations for Dynamic Risk Assessment Xabier Echeberria-Barrio Amaia Gil-Lerchundi Jon Egana-Zubia Raul Orduna Urrutia AAML 32 6 0 12 Feb 2024
A deep learning model for burn depth classification using ultrasound imaging Sangrock Lee Rahul Rahul James Lukan Tatiana Boyko Kateryna Zelenova Basiel Makled Conner Parsey Jack Norfleet S. De MedIm 16 13 0 29 Mar 2022
Layer-wise Regularized Adversarial Training using Layers Sustainability Analysis (LSA) framework Mohammad Khalooei M. Homayounpour M. Amirmazlaghani AAML 25 3 0 05 Feb 2022
Mitigating Gradient-based Adversarial Attacks via Denoising and Compression Rehana Mahfuz R. Sahay Aly El Gamal AAML 14 3 0 03 Apr 2021
A Generative Model based Adversarial Security of Deep Learning and Linear Classifier Models Ferhat Ozgur Catak Samed Sivaslioglu Kevser Sahinbas AAML 23 7 0 17 Oct 2020
Deep Learning Defenses Against Adversarial Examples for Dynamic Risk Assessment Xabier Echeberria-Barrio Amaia Gil-Lerchundi Ines Goicoechea-Telleria Raul Orduna Urrutia AAML 22 5 0 02 Jul 2020
Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks Ka-Ho Chow Wenqi Wei Yanzhao Wu Ling Liu AAML 25 15 0 21 Aug 2019
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks R. Sahay Rehana Mahfuz Aly El Gamal AAML 22 5 0 13 Jun 2019
Fast Deep Learning for Automatic Modulation Classification Sharan Ramjee Shengtai Ju Diyu Yang Xiaoyu Liu Aly El Gamal Yonina C. Eldar 30 145 0 16 Jan 2019