Adversarial Attack
Adversarial attacks aim to deceive machine learning models by subtly altering input data, causing misclassifications or other erroneous outputs. Current research focuses on developing more robust models and detection methods, exploring various attack strategies across different model architectures (including vision transformers, recurrent neural networks, and graph neural networks) and data types (images, text, signals, and tabular data). Understanding and mitigating these attacks is crucial for ensuring the reliability and security of AI systems in diverse applications, from autonomous vehicles to medical diagnosis and cybersecurity.
Papers
Test-time Detection and Repair of Adversarial Samples via Masked Autoencoder
Yun-Yun Tsai, Ju-Chin Chao, Albert Wen, Zhaoyuan Yang, Chengzhi Mao, Tapan Shah, Junfeng Yang
Revisiting DeepFool: generalization and improvement
Alireza Abdollahpourrostam, Mahed Abroshan, Seyed-Mohsen Moosavi-Dezfooli
Wasserstein Adversarial Examples on Univariant Time Series Data
Wenjie Wang, Li Xiong, Jian Lou
State-of-the-art optical-based physical adversarial attacks for deep learning computer vision systems
Junbin Fang, You Jiang, Canjian Jiang, Zoe L. Jiang, Siu-Ming Yiu, Chuanyi Liu
GNN-Ensemble: Towards Random Decision Graph Neural Networks
Wenqi Wei, Mu Qiao, Divyesh Jadav
Adversarial Attacks against Binary Similarity Systems
Gianluca Capozzi, Daniele Cono D'Elia, Giuseppe Antonio Di Luna, Leonardo Querzoni
Translate your gibberish: black-box adversarial attack on machine translation systems
Andrei Chertkov, Olga Tsymboi, Mikhail Pautov, Ivan Oseledets
Robust Mode Connectivity-Oriented Adversarial Defense: Enhancing Neural Network Robustness Against Diversified $\ell_p$ Attacks
Ren Wang, Yuxuan Li, Sijia Liu
Fuzziness-tuned: Improving the Transferability of Adversarial Examples
Xiangyuan Yang, Jie Lin, Hanlin Zhang, Xinyu Yang, Peng Zhao
Adversarial Counterfactual Visual Explanations
Guillaume Jeanneret, Loïc Simon, Frédéric Jurie
It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness
Peiyu Xiong, Michael Tegegn, Jaskeerat Singh Sarin, Shubhraneel Pal, Julia Rubin