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
Malacopula: adversarial automatic speaker verification attacks using a neural-based generalised Hammerstein model
Massimiliano Todisco, Michele Panariello, Xin Wang, Héctor Delgado, Kong Aik Lee, Nicholas Evans
Attack Anything: Blind DNNs via Universal Background Adversarial Attack
Jiawei Lian, Shaohui Mei, Xiaofei Wang, Yi Wang, Lefan Wang, Yingjie Lu, Mingyang Ma, Lap-Pui Chau
Training Verifiably Robust Agents Using Set-Based Reinforcement Learning
Manuel Wendl, Lukas Koller, Tobias Ladner, Matthias Althoff
Ask, Attend, Attack: A Effective Decision-Based Black-Box Targeted Attack for Image-to-Text Models
Qingyuan Zeng, Zhenzhong Wang, Yiu-ming Cheung, Min Jiang
Stochastic Bandits Robust to Adversarial Attacks
Xuchuang Wang, Jinhang Zuo, Xutong Liu, John C. S. Lui, Mohammad Hajiesmaili
ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scale
Xin Wang, Hector Delgado, Hemlata Tak, Jee-weon Jung, Hye-jin Shim, Massimiliano Todisco, Ivan Kukanov, Xuechen Liu, Md Sahidullah, Tomi Kinnunen, Nicholas Evans, Kong Aik Lee, Junichi Yamagishi
Towards Physical World Backdoor Attacks against Skeleton Action Recognition
Qichen Zheng, Yi Yu, Siyuan Yang, Jun Liu, Kwok-Yan Lam, Alex Kot
DFT-Based Adversarial Attack Detection in MRI Brain Imaging: Enhancing Diagnostic Accuracy in Alzheimer's Case Studies
Mohammad Hossein Najafi, Mohammad Morsali, Mohammadmahdi Vahediahmar, Saeed Bagheri Shouraki
Enhancing Adversarial Attacks via Parameter Adaptive Adversarial Attack
Zhibo Jin, Jiayu Zhang, Zhiyu Zhu, Chenyu Zhang, Jiahao Huang, Jianlong Zhou, Fang Chen
TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-cases
Thibault Simonetto, Salah Ghamizi, Maxime Cordy
Robust Active Learning (RoAL): Countering Dynamic Adversaries in Active Learning with Elastic Weight Consolidation
Ricky Maulana Fajri, Yulong Pei, Lu Yin, Mykola Pechenizkiy
Fooling SHAP with Output Shuffling Attacks
Jun Yuan, Aritra Dasgupta
Towards Adversarial Robustness via Debiased High-Confidence Logit Alignment
Kejia Zhang, Juanjuan Weng, Zhiming Luo, Shaozi Li
Multimodal Large Language Models for Phishing Webpage Detection and Identification
Jehyun Lee, Peiyuan Lim, Bryan Hooi, Dinil Mon Divakaran
Classifier Guidance Enhances Diffusion-based Adversarial Purification by Preserving Predictive Information
Mingkun Zhang, Jianing Li, Wei Chen, Jiafeng Guo, Xueqi Cheng