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

October 21, 2024

Conflict-Aware Adversarial Training
Zhiyu Xue, Haohan Wang, Yao Qin, Ramtin Pedarsani
Adversarial Training Adversarial Robustness Adversarial Sample Adversarial Loss

October 16, 2024

New Paradigm of Adversarial Training: Breaking Inherent Trade-Off between Accuracy and Robustness via Dummy Classes
Yanyun Wang, Li Liu, Zi Liang, Qingqing Ye, Haibo Hu
Native Robustness Adversarial Example Adversarial Training Adversarial Robustness Adversarial Sample Robust Learning Function Class

October 13, 2024

Understanding Robustness of Parameter-Efficient Tuning for Image Classification
Jiacheng Ruan, Xian Gao, Suncheng Xiang, Mingye Xie, Ting Liu, Yuzhuo Fu
Native Robustness Classification Code Adversarial Sample Parameter Efficient Tuning Learnable Parameter Perturbation Attack Natural Robustness Research

October 9, 2024

SEAL: Safety-enhanced Aligned LLM Fine-tuning via Bilevel Data Selection
Han Shen, Pin-Yu Chen, Payel Das, Tianyi Chen
Fine Tuning Bilevel Optimization Adversarial Sample LLM Fine Tuning Seal Generation

September 23, 2024

Improving Adversarial Robustness for 3D Point Cloud Recognition at Test-Time through Purified Self-Training
Jinpeng Lin, Xulei Yang, Tianrui Li, Xun Xu
Adversarial Attack Adversarial Training Adversarial Robustness Self Training Adversarial Sample Test Time Adversarial Purification Point Cloud Recognition

September 1, 2024

Comprehensive Botnet Detection by Mitigating Adversarial Attacks, Navigating the Subtleties of Perturbation Distances and Fortifying Predictions with Conformal Layers
Rahul Yumlembam, Biju Issac, Seibu Mary Jacob, Longzhi Yang
Adversarial Attack Adversarial Example Adversarial Sample Conformal FRP@TPR95 Metric Botnet Related Malware BotNet Detection

August 20, 2024

MsMemoryGAN: A Multi-scale Memory GAN for Palm-vein Adversarial Purification
Huafeng Qin, Yuming Fu, Huiyan Zhang, Mounim A. El-Yacoubi, Xinbo Gao, Qun Song, Jun Wang
Adversarial Attack Adversarial Perturbation Multi Scale Memory Trace Adversarial Sample

August 14, 2024

Enhancing Adversarial Attacks via Parameter Adaptive Adversarial Attack
Zhibo Jin, Jiayu Zhang, Zhiyu Zhu, Chenyu Zhang, Jiahao Huang, Jianlong Zhou, Fang Chen
Adversarial Attack Adversarial Sample Adversarial Parameter

August 8, 2024

Towards Resilient and Efficient LLMs: A Comparative Study of Efficiency, Performance, and Adversarial Robustness
Xiaojing Fan, Chunliang Tao
Large Language Model Adversarial Attack High Efficiency Adversarial Robustness System Performance Adversarial Sample Tiny Refinement Elicit Resilience

August 6, 2024

Sample-agnostic Adversarial Perturbation for Vision-Language Pre-training Models
Haonan Zheng, Wen Jiang, Xinyang Deng, Wenrui Li
Adversarial Attack Full Model Adversarial Sample Adversarial Patch Visual Language Task Multimodal Decision

August 5, 2024

On the Robustness of Malware Detectors to Adversarial Samples
Muhammad Salman, Benjamin Zi Hao Zhao, Hassan Jameel Asghar, Muhammad Ikram, Sidharth Kaushik, Mohamed Ali Kaafar
Adversarial Attack Native Robustness Adversarial Example Adversarial Sample Adversarial Malware Sensitive Hashing

July 30, 2024

DeepBaR: Fault Backdoor Attack on Deep Neural Network Layers
C. A. Martínez-Mejía, J. Solano, J. Breier, D. Bucko, X. Hou
Adversarial Sample CNN Architecture Neural Network Layer Backdoor Injection Fault Injection Attack Deep Attack

July 29, 2024

July 25, 2024

Network Inversion of Convolutional Neural Nets
Pirzada Suhail, Amit Sethi
Neural Network Inherent Interpretability Deep Convolutional Neural Network Adversarial Sample Network Inversion

July 13, 2024

SemiAdv: Query-Efficient Black-Box Adversarial Attack with Unlabeled Images
Mingyuan Fan, Yang Liu, Cen Chen, Ximeng Liu
Adversarial Attack Adversarial Sample Unlabeled Image Untargeted Adversarial Attack

June 24, 2024

Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by Evolving Adversarial Instances
Emma Hart, Quentin Renau, Kevin Sim, Mohamad Alissa
Deep Neural Network Native Robustness Adversarial Sample Algorithm Selection Deep Recurrent Neural Network Adversarial Instance

June 19, 2024

GraphMU: Repairing Robustness of Graph Neural Networks via Machine Unlearning
Tao Wu, Xinwen Cao, Chao Wang, Shaojie Qiao, Xingping Xian, Lin Yuan, Canyixing Cui, Yanbing Liu
Graph Neural Network Adversarial Attack Native Robustness Machine Unlearning Adversarial Sample Adversarial Defense

June 15, 2024

Robust Image Classification in the Presence of Out-of-Distribution and Adversarial Samples Using Attractors in Neural Networks
Nasrin Alipour, Seyyed Ali SeyyedSalehi
Neural Network Adversarial Attack Adversarial Example Image Classification Distribution Detection Distribution Data Adversarial Sample Robust Classification New Attractor

June 13, 2024

MirrorCheck: Efficient Adversarial Defense for Vision-Language Models
Samar Fares, Klea Ziu, Toluwani Aremu, Nikita Durasov, Martin Takáč, Pascal Fua, Karthik Nandakumar, Ivan Laptev
Adversarial Attack Vision Language Model Adversarial Sample Image Domain Adversarial Threat Efficient Adversarial Attack