Adversarial Example

Adversarial examples are subtly altered inputs designed to fool machine learning models, primarily deep neural networks (DNNs), into making incorrect predictions. Current research focuses on improving model robustness against these attacks, exploring techniques like ensemble methods, multi-objective representation learning, and adversarial training, often applied to architectures such as ResNets and Vision Transformers. Understanding and mitigating the threat of adversarial examples is crucial for ensuring the reliability and security of AI systems across diverse applications, from image classification and natural language processing to malware detection and autonomous driving. The development of robust defenses and effective attack detection methods remains a significant area of ongoing investigation.

Papers

October 5, 2023

An Integrated Algorithm for Robust and Imperceptible Audio Adversarial Examples
Armin Ettenhofer, Jan-Philipp Schulze, Karla Pizzi
Automatic Speech Recognition Adversarial Example Robust Version Impulse Response Adversarial Audio Psychoacoustic Model Unified Algorithm Audio Adversarial Example

October 4, 2023

Misusing Tools in Large Language Models With Visual Adversarial Examples
Xiaohan Fu, Zihan Wang, Shuheng Li, Rajesh K. Gupta, Niloofar Mireshghallah, Taylor Berg-Kirkpatrick, Earlence Fernandes
Adversarial Example Adversarial Image Code Syntax Tool Usage

October 3, 2023

Splitting the Difference on Adversarial Training
Matan Levi, Aryeh Kontorovich
Native Robustness Adversarial Example Adversarial Training Qualitative Difference Robust Model Split and Fit Optimal Accuracy

October 2, 2023

October 1, 2023

September 30, 2023

September 29, 2023

September 28, 2023

Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness
Ambar Pal, Jeremias Sulam, René Vidal
Adversarial Example Adversarial Robustness Integral Role Robustness Guarantee Robust Classifier High Quality Subset Text Consolidation

September 27, 2023

On the Computational Entanglement of Distant Features in Adversarial Machine Learning
YenLung Lai, Xingbo Dong, Zhe Jin
Adversarial Example Adversarial Learning Asset Correlation

September 26, 2023

DifAttack: Query-Efficient Black-Box Attack via Disentangled Feature Space
Liu Jun, Zhou Jiantao, Zeng Jiandian, Jinyu Tian
Adversarial Attack Adversarial Example Feature Space Black Box Attack Score Based Adversarial Feature Adversarial Capability

September 25, 2023

Adversarial Attacks on Video Object Segmentation with Hard Region Discovery
Ping Li, Yu Zhang, Li Yuan, Jian Zhao, Xianghua Xu, Xiaoqin Zhang
Adversarial Attack Adversarial Example Video Object Segmentation Region Selection ImageNet Attack

September 22, 2023

Improving Machine Learning Robustness via Adversarial Training
Long Dang, Thushari Hapuarachchi, Kaiqi Xiong, Jing Lin
Adversarial Attack Native Robustness Adversarial Example Adversarial Training Machine Learning Robustness

September 21, 2023

September 20, 2023

PRAT: PRofiling Adversarial aTtacks
Rahul Ambati, Naveed Akhtar, Ajmal Mian, Yogesh Singh Rawat
Adversarial Attack Adversarial Example Adversarial Sample Adversarial Datasets

September 19, 2023

What Learned Representations and Influence Functions Can Tell Us About Adversarial Examples
Shakila Mahjabin Tonni, Mark Dras
Deep Neural Network Adversarial Example Learned Representation Influence Function Input Perturbation