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

January 17, 2024

Rethinking Impersonation and Dodging Attacks on Face Recognition Systems
Fengfan Zhou, Qianyu Zhou, Bangjie Yin, Hui Zheng, Xuequan Lu, Lizhuang Ma, Hefei Ling
Adversarial Attack Adversarial Example Adversarial Perturbation Context Impersonation

January 13, 2024

Exploring Adversarial Attacks against Latent Diffusion Model from the Perspective of Adversarial Transferability
Junxi Chen, Junhao Dong, Xiaohua Xie
Adversarial Attack Adversarial Example Latent Diffusion Model Visual Perspective Differentiable Surrogate

January 12, 2024

Adversarial Examples are Misaligned in Diffusion Model Manifolds
Peter Lorenz, Ricard Durall, Janis Keuper
Diffusion Model Adversarial Attack Adversarial Example Adversarial Robustness Adversarial Instance Manifold Diffusion

January 11, 2024

GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative model
Zhiyu Zhu, Huaming Chen, Xinyi Wang, Jiayu Zhang, Zhibo Jin, Kim-Kwang Raymond Choo, Jun Shen, Dong Yuan
Generative Adversarial Network Adversarial Example Task Transferability Adversarial Sample Transferable Adversarial Attack

January 6, 2024

Data-Dependent Stability Analysis of Adversarial Training
Yihan Wang, Shuang Liu, Xiao-Shan Gao
Adversarial Example Adversarial Training Generalization Bound Robust Generalization Stability Analysis Robust Generalization Gap

January 5, 2024

January 4, 2024

Demonstration of an Adversarial Attack Against a Multimodal Vision Language Model for Pathology Imaging
Poojitha Thota, Jai Prakash Veerla, Partha Sai Guttikonda, Mohammad S. Nasr, Shirin Nilizadeh, Jacob M. Luber
Adversarial Attack Adversarial Example Adversarial Perturbation Textual Demonstration Robust Defense Adversarial Manipulation Multimodal Vision Language Model Pathology Imaging

January 2, 2024

JMA: a General Algorithm to Craft Nearly Optimal Targeted Adversarial Example
Benedetta Tondi, Wei Guo, Mauro Barni
Adversarial Example Multi Label Classification Mahalanobis Distance General Algorithm

December 26, 2023

From Text to Multimodal: A Comprehensive Survey of Adversarial Example Generation in Question Answering Systems
Gulsum Yigit, Mehmet Fatih Amasyali
Adversarial Example Text Modality Comprehensive Survey Multimodal Phenomenon Question Answering System Adversarial Question Adversarial Challenge

December 21, 2023

December 20, 2023

December 18, 2023

December 16, 2023

Perturbation-Invariant Adversarial Training for Neural Ranking Models: Improving the Effectiveness-Robustness Trade-Off
Yu-An Liu, Ruqing Zhang, Mingkun Zhang, Wei Chen, Maarten de Rijke, Jiafeng Guo, Xueqi Cheng
Adversarial Attack Adversarial Example Adversarial Robustness Adversarial DEfense Neural Ranker Robustness Trade

Adversarial Example

Papers

Rethinking Impersonation and Dodging Attacks on Face Recognition Systems

Robust Localization of Key Fob Using Channel Impulse Response of Ultra Wide Band Sensors for Keyless Entry Systems

Bag of Tricks to Boost Adversarial Transferability

A Generative Adversarial Attack for Multilingual Text Classifiers

Exploring Adversarial Attacks against Latent Diffusion Model from the Perspective of Adversarial Transferability

Adversarial Examples are Misaligned in Diffusion Model Manifolds

GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative model

Data-Dependent Stability Analysis of Adversarial Training

Enhancing targeted transferability via feature space fine-tuning

A Random Ensemble of Encrypted models for Enhancing Robustness against Adversarial Examples

Demonstration of an Adversarial Attack Against a Multimodal Vision Language Model for Pathology Imaging

JMA: a General Algorithm to Craft Nearly Optimal Targeted Adversarial Example

From Text to Multimodal: A Comprehensive Survey of Adversarial Example Generation in Question Answering Systems

AutoAugment Input Transformation for Highly Transferable Targeted Attacks

Where and How to Attack? A Causality-Inspired Recipe for Generating Counterfactual Adversarial Examples

LRS: Enhancing Adversarial Transferability through Lipschitz Regularized Surrogate

PGN: A perturbation generation network against deep reinforcement learning

The Ultimate Combo: Boosting Adversarial Example Transferability by Composing Data Augmentations

The Pros and Cons of Adversarial Robustness

Perturbation-Invariant Adversarial Training for Neural Ranking Models: Improving the Effectiveness-Robustness Trade-Off