Traditional Adversarial

Traditional adversarial attacks aim to deceive machine learning models by subtly altering input data, causing misclassifications while remaining largely imperceptible to humans. Current research focuses on developing more effective attacks across various data types (images, text, graphs, radio signals) and model architectures (CNNs, GNNs, LLMs, quantum models), often employing techniques like generative models, saliency maps, and semantic manipulation to craft these adversarial examples. This field is crucial for evaluating and improving the robustness of machine learning systems, with implications for security, reliability, and the ethical deployment of AI in diverse applications.

Papers

September 23, 2024

Log-normal Mutations and their Use in Detecting Surreptitious Fake Images
Ismail Labiad, Thomas Bäck, Pierre Fernandez, Laurent Najman, Tom Sanders, Furong Ye, Mariia Zameshina, Olivier Teytaud
Adversarial Attack Greater Public Use Traditional Adversarial Sigma Lognormal

June 28, 2024

Deceptive Diffusion: Generating Synthetic Adversarial Examples
Lucas Beerens, Catherine F. Higham, Desmond J. Higham
Adversarial Example Adversarial Image Deceptive Diffusion Traditional Adversarial

June 18, 2024

Saliency Attention and Semantic Similarity-Driven Adversarial Perturbation
Hetvi Waghela, Jaydip Sen, Sneha Rakshit
Adversarial Example Textual Adversarial Attack Traditional Adversarial Word Saliency

April 24, 2024

A General Black-box Adversarial Attack on Graph-based Fake News Detectors
Peican Zhu, Zechen Pan, Yang Liu, Jiwei Tian, Keke Tang, Zhen Wang
Graph Neural Network Fake News Adversarial Framework Black Box Adversarial Attack Traditional Adversarial Fake User

March 17, 2024

A Modified Word Saliency-Based Adversarial Attack on Text Classification Models
Hetvi Waghela, Sneha Rakshit, Jaydip Sen
Adversarial Attack Adversarial Example Adversarial Learning Text Classification Model Traditional Adversarial

February 13, 2024

Enhancing Robustness of Indoor Robotic Navigation with Free-Space Segmentation Models Against Adversarial Attacks
Qiyuan An, Christos Sevastopoulos, Fillia Makedon
Adversarial Attack Native Robustness Adversarial Perturbation Adversarial Image Hidden Layer Traditional Adversarial Indoor Robot Navigation Free Space Segmentation

February 5, 2024

Transcending Adversarial Perturbations: Manifold-Aided Adversarial Examples with Legitimate Semantics
Shuai Li, Xiaoyu Jiang, Xiaoguang Ma
Adversarial Example Adversarial Perturbation Traditional Adversarial Manifold Adversarial

January 29, 2024

Transparency Attacks: How Imperceptible Image Layers Can Fool AI Perception
Forrest McKee, David Noever
Hidden Layer Steganographic Performance Digital STEALTH Metric Traditional Adversarial Leakage Attack Machine Perception Image Layer

December 13, 2023

Radio Signal Classification by Adversarially Robust Quantum Machine Learning
Yanqiu Wu, Eromanga Adermann, Chandra Thapa, Seyit Camtepe, Hajime Suzuki, Muhammad Usman
Adversarial Attack Quantum Machine Learning Signal Classification Traditional Adversarial

December 5, 2023

Prompt Optimization via Adversarial In-Context Learning
Xuan Long Do, Yiran Zhao, Hannah Brown, Yuxi Xie, James Xu Zhao, Nancy F. Chen, Kenji Kawaguchi, Michael Shieh, Junxian He
Context Learning Prompt Optimization Adversarial Loss Traditional Adversarial

December 4, 2023

Adversarial Medical Image with Hierarchical Feature Hiding
Qingsong Yao, Zecheng He, Yuexiang Li, Yi Lin, Kai Ma, Yefeng Zheng, S. Kevin Zhou
Adversarial Example Traditional Adversarial Adversarial Watermarking Adversarial Feature

October 23, 2023

F$^2$AT: Feature-Focusing Adversarial Training via Disentanglement of Natural and Perturbed Patterns
Yaguan Qian, Chenyu Zhao, Zhaoquan Gu, Bin Wang, Shouling Ji, Wei Wang, Boyang Zhou, Pan Zhou
Adversarial Example Adversarial Training Adversarial Robustness Disentanglement Capability Adversarial Noise Visual Naturalness Traditional Adversarial Unintentional Perturbation

September 14, 2023

Semantic Adversarial Attacks via Diffusion Models
Chenan Wang, Jinhao Duan, Chaowei Xiao, Edward Kim, Matthew Stamm, Kaidi Xu
Diffusion Model Adversarial Perturbation Semantic Adversarial Traditional Adversarial Latent Mask

May 24, 2023

Fast Adversarial CNN-based Perturbation Attack on No-Reference Image- and Video-Quality Metrics
Ekaterina Shumitskaya, Anastasia Antsiferova, Dmitriy Vatolin
Adversarial Perturbation Reference Image Fast Adversarial Training Traditional Adversarial Video Quality Metric Reference Quality Assessment Perturbation Attack Reference Quality

May 18, 2023

Adversarial Amendment is the Only Force Capable of Transforming an Enemy into a Friend
Chong Yu, Tao Chen, Zhongxue Gan
Adversarial Attack Adversarial Robustness Traditional Adversarial Enemy Spotted Adversarial Change

February 27, 2023

GLOW: Global Layout Aware Attacks on Object Detection
Buyu Liu, BaoJun, Jianping Fan, Xi Peng, Kui Ren, Jun Yu
Adversarial Attack Traditional Adversarial Context Consistency

December 19, 2022

Discrete Point-wise Attack Is Not Enough: Generalized Manifold Adversarial Attack for Face Recognition
Qian Li, Yuxiao Hu, Ye Liu, Dongxiao Zhang, Xin Jin, Yuntian Chen
Adversarial Example Face Recognition Semantic Adversarial Traditional Adversarial Manifold Adversarial

November 23, 2022

Benchmarking Adversarially Robust Quantum Machine Learning at Scale
Maxwell T. West, Sarah M. Erfani, Christopher Leckie, Martin Sevior, Lloyd C. L. Hollenberg, Muhammad Usman
Adversarial Attack Quantum Machine Learning Visual Analogue Scale Quantum Advantage Traditional Adversarial

November 10, 2022

Robust Smart Home Face Recognition under Starving Federated Data
Jaechul Roh, Yajun Fang
Adversarial Attack Deep Learning Model Adversarial Training Traditional Adversarial Federated Data

March 23, 2022

Adversarial Training for Improving Model Robustness? Look at Both Prediction and Interpretation
Hanjie Chen, Yangfeng Ji
Adversarial Example Adversarial Training Human Prediction Model Robustness Abstract Interpretation Traditional Adversarial