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

December 2, 2024

Traversing the Subspace of Adversarial Patches
Jens Bayer, Stefan Becker, David Münch, Michael Arens, Jürgen Beyerer
Adversarial Example Adversarial Training Dimensionality Reduction Adversarial Patch Low Dimensional Manifold Subspace Prototype

December 1, 2024

Exposing LLM Vulnerabilities: Adversarial Scam Detection and Performance
Chen-Wei Chang, Shailik Sarkar, Shutonu Mitra, Qi Zhang, Hossein Salemi, Hemant Purohit, Fengxiu Zhang, Michin Hong, Jin-Hee Cho, Chang-Tien Lu
Adversarial Example System Performance Adversarial Text LLM Safety Adversarial Detection

November 29, 2024

Towards Class-wise Robustness Analysis
Tejaswini Medi, Julia Grabinski, Margret Keuper
Adversarial Attack Adversarial Example Adversarial Training Robustness Evaluation Robust Neural Network

November 27, 2024

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation
Tian Ye, Rajgopal Kannan, Viktor Prasanna
Adversarial Attack Adversarial Example Adversarial Training Interpolation Regime Semi Supervised Adversarial

November 25, 2024

November 24, 2024

Chain of Attack: On the Robustness of Vision-Language Models Against Transfer-Based Adversarial Attacks
Peng Xie, Yequan Bie, Jianda Mao, Yangqiu Song, Yang Wang, Hao Chen, Kani Chen
Adversarial Attack Native Robustness Vision Language Model Adversarial Example Side Chain Adversarial Transferability Blackbox Attack

November 23, 2024

November 22, 2024

Gradient Masking All-at-Once: Ensemble Everything Everywhere Is Not Robust
Jie Zhang, Kristina Nikolić, Nicholas Carlini, Florian Tramèr
Adversarial Example Image Classifier Threat Model Robust Classification Gradient Masking

November 21, 2024

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders
Alexander Stevens, Jari Peeperkorn, Johannes De Smedt, Jochen De Weerdt
Adversarial Attack Adversarial Example Variational Autoencoders Business Process Predictive Process Monitoring ImageNet Attack

November 18, 2024

Theoretical Corrections and the Leveraging of Reinforcement Learning to Enhance Triangle Attack
Nicole Meng, Caleb Manicke, David Chen, Yingjie Lao, Caiwen Ding, Pengyu Hong, Kaleel Mahmood
Reinforcement Learning Adversarial Example Cyber Attack Supervised Correction

November 15, 2024

A Hard-Label Cryptanalytic Extraction of Non-Fully Connected Deep Neural Networks using Side-Channel Attacks
Benoit Coqueret, Mathieu Carbone, Olivier Sentieys, Gabriel Zaid
Neural Network Deep Neural Network Adversarial Example Network Programming Deep Network Side Channel Hard Label Attack

November 12, 2024

November 11, 2024

Adversarial Detection with a Dynamically Stable System
Xiaowei Long, Jie Lin, Xiangyuan Yang
Adversarial Example Adversarial Detection

November 7, 2024

Neural Fingerprints for Adversarial Attack Detection
Haim Fisher, Moni Shahar, Yehezkel S. Resheff
Deep Learning Model Adversarial Example Image Classification Supervised ImageNet Adversarial Attack Detection

November 5, 2024

Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Training
Junhao Dong, Xinghua Qu, Z. Jane Wang, Yew-Soon Ong
Adversarial Example Adversarial Training Adversarial Robustness Domain Adversarial

November 4, 2024

Semantic-Aligned Adversarial Evolution Triangle for High-Transferability Vision-Language Attack
Xiaojun Jia, Sensen Gao, Qing Guo, Ke Ma, Yihao Huang, Simeng Qin, Yang Liu, Ivor Tsang Fellow, Xiaochun Cao
Adversarial Attack Adversarial Example Task Transferability Sentence Level Attack Adversarial Edge

Adversarial Example

Papers

Traversing the Subspace of Adversarial Patches

Exposing LLM Vulnerabilities: Adversarial Scam Detection and Performance

Towards Class-wise Robustness Analysis

Adversarial Training in Low-Label Regimes with Margin-Based Interpolation

Imperceptible Adversarial Examples in the Physical World

Unlocking The Potential of Adaptive Attacks on Diffusion-Based Purification

Scaling Laws for Black box Adversarial Attacks

Chain of Attack: On the Robustness of Vision-Language Models Against Transfer-Based Adversarial Attacks

Enhancing the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation

Improving Transferable Targeted Attacks with Feature Tuning Mixup

Gradient Masking All-at-Once: Ensemble Everything Everywhere Is Not Robust

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders

Theoretical Corrections and the Leveraging of Reinforcement Learning to Enhance Triangle Attack

A Hard-Label Cryptanalytic Extraction of Non-Fully Connected Deep Neural Networks using Side-Channel Attacks

IAE: Irony-based Adversarial Examples for Sentiment Analysis Systems

Chain Association-based Attacking and Shielding Natural Language Processing Systems

Adversarial Detection with a Dynamically Stable System

Neural Fingerprints for Adversarial Attack Detection

Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Training

Semantic-Aligned Adversarial Evolution Triangle for High-Transferability Vision-Language Attack