Adversarial Sample

Adversarial samples are inputs designed to intentionally mislead machine learning models, primarily by introducing small, imperceptible perturbations to otherwise correctly classified data. Current research focuses on developing more robust models through techniques like adversarial training, purification methods using generative models (e.g., GANs), and exploring the vulnerabilities of various architectures, including convolutional neural networks, recurrent networks, and large language models. Understanding and mitigating the impact of adversarial samples is crucial for ensuring the reliability and security of machine learning systems across diverse applications, from cybersecurity to medical diagnosis.

Papers

May 16, 2022

Robust Representation via Dynamic Feature Aggregation
Haozhe Liu, Haoqin Ji, Yuexiang Li, Nanjun He, Haoqian Wu, Feng Liu, Linlin Shen, Yefeng Zheng
Adversarial Attack Deep Convolutional Neural Network Adversarial Sample Robust Representation Feature Aggregation

May 5, 2022

Can collaborative learning be private, robust and scalable?
Dmitrii Usynin, Helena Klause, Johannes C. Paetzold, Daniel Rueckert, Georgios Kaissis
Differential Privacy Adversarial Training Adversarial Sample Collaborative Learning Adversarial Data Robust Adversarial

May 2, 2022

Enhancing Adversarial Training with Feature Separability
Yaxin Li, Xiaorui Liu, Han Xu, Wentao Wang, Jiliang Tang
Adversarial Attack Adversarial Training Adversarial Sample Adversarial Graph Feature Separation

April 21, 2022

Fast AdvProp
Jieru Mei, Yucheng Han, Yutong Bai, Yixiao Zhang, Yingwei Li, Xianhang Li, Alan Yuille, Cihang Xie
Adversarial Example Adversarial Learning Adversarial Sample

April 9, 2022

Understanding, Detecting, and Separating Out-of-Distribution Samples and Adversarial Samples in Text Classification
Cheng-Han Chiang, Hung-yi Lee
Data Detection Text Classification Human Understanding Distribution Data Adversarial Sample Distribution Sample Hidden Representation Text Classification Model

April 7, 2022

Adaptive-Gravity: A Defense Against Adversarial Samples
Ali Mirzaeian, Zhi Tian, Sai Manoj P D, Banafsheh S. Latibari, Ioannis Savidis, Houman Homayoun, Avesta Sasan
Adversarial Attack Adversarial Example Adversarial Sample Gravity Compensation

April 4, 2022

DAD: Data-free Adversarial Defense at Test Time
Gaurav Kumar Nayak, Ruchit Rawal, Anirban Chakraborty
Adversarial Attack Adversarial Training Adversarial Sample Test Time

March 30, 2022

StyleFool: Fooling Video Classification Systems via Style Transfer
Yuxin Cao, Xi Xiao, Ruoxi Sun, Derui Wang, Minhui Xue, Sheng Wen
Adversarial Attack Adversarial Perturbation Style Transfer Black Box Adversarial Sample Video Classification

March 26, 2022

A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies
Zhuang Qian, Kaizhu Huang, Qiu-Feng Wang, Xu-Yao Zhang
Timely Survey Adversarial Attack Adversarial Example Adversarial Training NCD Method Theoretical Understanding Adversarial Sample Pattern Recognition Robust Adversarial Training

March 21, 2022

A Prompting-based Approach for Adversarial Example Generation and Robustness Enhancement
Yuting Yang, Pei Huang, Juan Cao, Jintao Li, Yun Lin, Jin Song Dong, Feifei Ma, Jian Zhang
Adversarial Attack Adversarial Example Adversarial Sample Adversarial Prompt Prompting Based Robustness Enhancement

March 19, 2022

Distinguishing Non-natural from Natural Adversarial Samples for More Robust Pre-trained Language Model
Jiayi Wang, Rongzhou Bao, Zhuosheng Zhang, Hai Zhao
Adversarial Attack Pre Trained Language Model Adversarial Sample Natural Adversarial Sample

March 18, 2022

March 11, 2022

An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attacks
Anirudh Yadav, Ashutosh Upadhyay, S. Sharanya
Adversarial Attack Adversarial Sample White Box Auto Encoder Defense Algorithm Automatic Coding Defense Strategy

March 1, 2022

Adversarial samples for deep monocular 6D object pose estimation
Jinlai Zhang, Weiming Li, Shuang Liang, Hao Wang, Jihong Zhu
Adversarial Example Estimation Task Adversarial Sample 6D Object

February 25, 2022

Projective Ranking-based GNN Evasion Attacks
He Zhang, Xingliang Yuan, Chuan Zhou, Shirui Pan
Adversarial Attack Adversarial Sample Attack Strategy Adversarial Evasion Attack Projective Simulation Learning Based Attack

February 21, 2022

HoneyModels: Machine Learning Honeypots
Ahmed Abdou, Ryan Sheatsley, Yohan Beugin, Tyler Shipp, Patrick McDaniel
Adversarial Example Adversarial Learning Adversarial Sample Adversary Agent Honeypot Interface

February 15, 2022

Unreasonable Effectiveness of Last Hidden Layer Activations for Adversarial Robustness
Omer Faruk Tuna, Ferhat Ozgur Catak, M. Taner Eskil
Loss Function Adversarial Robustness Adversarial Sample Standard Deep Adversarial Evasion Attack Deep Attack

February 4, 2022

Pixle: a fast and effective black-box attack based on rearranging pixels
Jary Pomponi, Simone Scardapane, Aurelio Uncini
Adversarial Attack Black Box Adversarial Sample Tetromino Pixel Adversarial Text

January 21, 2022

The Security of Deep Learning Defences for Medical Imaging
Moshe Levy, Guy Amit, Yuval Elovici, Yisroel Mirsky
Deep Learning Deep Neural Network Deep Learning Model Medical Imaging Adversarial Sample Security Related