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

November 26, 2022

Supervised Contrastive Prototype Learning: Augmentation Free Robust Neural Network
Iordanis Fostiropoulos, Laurent Itti
Contrastive Learning Adversarial Example Contrastive Loss Adversarial Sample Robust Data

November 20, 2022

Feature Weaken: Vicinal Data Augmentation for Classification
Songhao Jiang, Yan Chu, Tianxing Ma, Tianning Zang
Classification Code Adversarial Sample Novel Training Feature Mixing Non Robust Feature

November 19, 2022

Towards Robust Dataset Learning
Yihan Wu, Xinda Li, Florian Kerschbaum, Heng Huang, Hongyang Zhang
Adversarial Training Adversarial Sample Robust Dataset

November 10, 2022

Test-time adversarial detection and robustness for localizing humans using ultra wide band channel impulse responses
Abhiram Kolli, Muhammad Jehanzeb Mirza, Horst Possegger, Horst Bischof
Adversarial Attack Native Robustness Adversarial Example Real Human Adversarial Sample Test Time Adversarial Detection Adversarial Example Detection

November 4, 2022

Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning
Anaelia Ovalle, Evan Czyzycki, Cho-Jui Hsieh
Adversarial Robustness Adversarial Sample Deep Metric Learning Network Sensitivity Adversarial Regularization

November 3, 2022

Robust Few-shot Learning Without Using any Adversarial Samples
Gaurav Kumar Nayak, Ruchit Rawal, Inder Khatri, Anirban Chakraborty
Meta Learning Adversarial Sample Adversarial Noise Adversarial Evaluation ROBust Few Shot

October 24, 2022

Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning
Ying Yuan, Giovanni Apruzzese, Mauro Conti
Machine Learning Adversarial Attack Adversarial Perturbation Adversarial Learning Adversarial Sample Anti Phishing

October 19, 2022

Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP
Yangyi Chen, Hongcheng Gao, Ganqu Cui, Fanchao Qi, Longtao Huang, Zhiyuan Liu, Maosong Sun
Adversarial Perturbation Adversarial Sample NLP Research Adversarial Objective Textual Adversarial Example Research Paradigm Adversarial Natural Language

October 12, 2022

Robust Models are less Over-Confident
Julia Grabinski, Paul Gavrikov, Janis Keuper, Margret Keuper
Adversarial Attack Adversarial Training Adversarial Robustness Adversarial Sample Robust Model

October 6, 2022

Are Synonym Substitution Attacks Really Synonym Substitution Attacks?
Cheng-Han Chiang, Hung-yi Lee
Adversarial Sample Synonym Substitution Attack Word Substitution

September 28, 2022

Attacking Compressed Vision Transformers
Swapnil Parekh, Devansh Shah, Pratyush Shukla
Adversarial Attack Vision Transformer State of the Art Model Compression Adversarial Sample Vision Transformer Compression

August 31, 2022

Unrestricted Adversarial Samples Based on Non-semantic Feature Clusters Substitution
MingWei Zhou, Xiaobing Pei
Adversarial Example Adversarial Sample Semantic Feature Defense Method Unrestricted Adversarial Deep Neural Network Classifier

July 31, 2022

Robust Real-World Image Super-Resolution against Adversarial Attacks
Jiutao Yue, Haofeng Li, Pengxu Wei, Guanbin Li, Liang Lin
Adversarial Attack Adversarial Sample Real World Image Super Resolution Random Masking Super Resolution Network

July 19, 2022

Decorrelative Network Architecture for Robust Electrocardiogram Classification
Christopher Wiedeman, Ge Wang
Adversarial Attack Adversarial Sample Feature Decorrelation ECG Classification Novel Ensemble Bayesian Deep Bayesian Ensemble

July 18, 2022

Adversarial Contrastive Learning via Asymmetric InfoNCE
Qiying Yu, Jieming Lou, Xianyuan Zhan, Qizhang Li, Wangmeng Zuo, Yang Liu, Jingjing Liu
Contrastive Learning Adversarial Robustness Adversarial Perturbation Adversarial Learning Adversarial Sample Information Asymmetry Adversarial Contrastive Learning

July 1, 2022

Efficient Adversarial Training With Data Pruning
Maximilian Kaufmann, Yiren Zhao, Ilia Shumailov, Robert Mullins, Nicolas Papernot
Adversarial Example Adversarial Training Adversarial Sample Data Pruning Adversarial Evaluation Efficient Adversarial Training

June 17, 2022

Detecting Adversarial Examples in Batches -- a geometrical approach
Danush Kumar Venkatesh, Peter Steinbach
Adversarial Attack Native Robustness Adversarial Example Adversarial Sample Large Batch Geometric Approach Geodesic Metric Space

May 31, 2022

May 22, 2022

Phrase-level Textual Adversarial Attack with Label Preservation
Yibin Lei, Yu Cao, Dianqi Li, Tianyi Zhou, Meng Fang, Mykola Pechenizkiy
Adversarial Sample Latent Vulnerability Level Perturbation Textual Adversarial Example