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

February 21, 2023

February 19, 2023

Stationary Point Losses for Robust Model
Weiwei Gao, Dazhi Zhang, Yao Li, Zhichang Guo, Ovanes Petrosian
Adversarial Attack Native Robustness Adversarial Example Cross Entropy Robust Model Loss Design Boundary Localization

February 16, 2023

February 14, 2023

Regret-Based Defense in Adversarial Reinforcement Learning
Roman Belaire, Pradeep Varakantham, Thanh Nguyen, David Lo
Reinforcement Learning Deep Reinforcement Learning Adversarial Example Adversarial Reinforcement Learning Robust Deep Reinforcement Learning

February 13, 2023

Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic Data
Gorka Abad, Oguzhan Ersoy, Stjepan Picek, Aitor Urbieta
Deep Neural Network Adversarial Example Spiking Neural Network Neuromorphic Datasets Stealthy Backdoor Attack

February 12, 2023

TextDefense: Adversarial Text Detection based on Word Importance Entropy
Lujia Shen, Xuhong Zhang, Shouling Ji, Yuwen Pu, Chunpeng Ge, Xing Yang, Yanghe Feng
Adversarial Attack Adversarial Example Adversarial Text Textual Adversarial Attack Text Attack Lexicon Entropy

February 10, 2023

February 9, 2023

February 8, 2023

WAT: Improve the Worst-class Robustness in Adversarial Training
Boqi Li, Weiwei Liu
Adversarial Attack Adversarial Example Adversarial Training Worst Case Robustness

February 7, 2023

Toward Face Biometric De-identification using Adversarial Examples
Mahdi Ghafourian, Julian Fierrez, Luis Felipe Gomez, Ruben Vera-Rodriguez, Aythami Morales, Zohra Rezgui, Raymond Veldhuis
Adversarial Example Face Recognition Adversarial Method Imperceptible Adversarial Perturbation Face De Identification

February 6, 2023

Less is More: Understanding Word-level Textual Adversarial Attack via n-gram Frequency Descend
Ning Lu, Shengcai Liu, Zhirui Zhang, Qi Wang, Haifeng Liu, Ke Tang
Adversarial Example Adversarial Training N Gram Textual Adversarial Attack Word Level Attack

February 4, 2023

A Minimax Approach Against Multi-Armed Adversarial Attacks Detection
Federica Granese, Marco Romanelli, Siddharth Garg, Pablo Piantanida
Adversarial Example Minimax Optimization Pre Training Data Detection Adversarial Attack Detection

February 2, 2023

TransFool: An Adversarial Attack against Neural Machine Translation Models
Sahar Sadrizadeh, Ljiljana Dolamic, Pascal Frossard
Adversarial Attack Adversarial Example Neural Machine Translation Adversarial Text Neural Machine Translation Model

February 1, 2023

Universal Soldier: Using Universal Adversarial Perturbations for Detecting Backdoor Attacks
Xiaoyun Xu, Oguzhan Ersoy, Stjepan Picek
Adversarial Attack Adversarial Example Backdoor Attack Backdoor Trigger Backdoor Detection Universal Robot