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
Baseline Defenses for Adversarial Attacks Against Aligned Language Models
Neel Jain, Avi Schwarzschild, Yuxin Wen, Gowthami Somepalli, John Kirchenbauer, Ping-yeh Chiang, Micah Goldblum, Aniruddha Saha, Jonas Geiping, Tom Goldstein
Why do universal adversarial attacks work on large language models?: Geometry might be the answer
Varshini Subhash, Anna Bialas, Weiwei Pan, Finale Doshi-Velez
Everything Perturbed All at Once: Enabling Differentiable Graph Attacks
Haoran Liu, Bokun Wang, Jianling Wang, Xiangjue Dong, Tianbao Yang, James Caverlee
Imperceptible Adversarial Attack on Deep Neural Networks from Image Boundary
Fahad Alrasheedi, Xin Zhong
A Classification-Guided Approach for Adversarial Attacks against Neural Machine Translation
Sahar Sadrizadeh, Ljiljana Dolamic, Pascal Frossard
Advancing Adversarial Robustness Through Adversarial Logit Update
Hao Xuan, Peican Zhu, Xingyu Li
A Survey of Graph Unlearning
Anwar Said, Tyler Derr, Mudassir Shabbir, Waseem Abbas, Xenofon Koutsoukos
On-Manifold Projected Gradient Descent
Aaron Mahler, Tyrus Berry, Tom Stephens, Harbir Antil, Michael Merritt, Jeanie Schreiber, Ioannis Kevrekidis
LCANets++: Robust Audio Classification using Multi-layer Neural Networks with Lateral Competition
Sayanton V. Dibbo, Juston S. Moore, Garrett T. Kenyon, Michael A. Teti
Sample Complexity of Robust Learning against Evasion Attacks
Pascale Gourdeau
Efficient Transfer Learning in Diffusion Models via Adversarial Noise
Xiyu Wang, Baijiong Lin, Daochang Liu, Chang Xu
Does Physical Adversarial Example Really Matter to Autonomous Driving? Towards System-Level Effect of Adversarial Object Evasion Attack
Ningfei Wang, Yunpeng Luo, Takami Sato, Kaidi Xu, Qi Alfred Chen