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
Defense Against Adversarial Attacks using Convolutional Auto-Encoders
Shreyasi Mandal
On the Robustness of Large Multimodal Models Against Image Adversarial Attacks
Xuanming Cui, Alejandro Aparcedo, Young Kyun Jang, Ser-Nam Lim
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System
Xinwei Yuan, Shu Han, Wei Huang, Hongliang Ye, Xianglong Kong, Fan Zhang
ScAR: Scaling Adversarial Robustness for LiDAR Object Detection
Xiaohu Lu, Hayder Radha
Realistic Scatterer Based Adversarial Attacks on SAR Image Classifiers
Tian Ye, Rajgopal Kannan, Viktor Prasanna, Carl Busart, Lance Kaplan
Scaling Laws for Adversarial Attacks on Language Model Activations
Stanislav Fort
FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks
Marc Vucovich, Devin Quinn, Kevin Choi, Christopher Redino, Abdul Rahman, Edward Bowen
InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language Models
Xunguang Wang, Zhenlan Ji, Pingchuan Ma, Zongjie Li, Shuai Wang
Singular Regularization with Information Bottleneck Improves Model's Adversarial Robustness
Guanlin Li, Naishan Zheng, Man Zhou, Jie Zhang, Tianwei Zhang
QuantAttack: Exploiting Dynamic Quantization to Attack Vision Transformers
Amit Baras, Alon Zolfi, Yuval Elovici, Asaf Shabtai
Rethinking PGD Attack: Is Sign Function Necessary?
Junjie Yang, Tianlong Chen, Xuxi Chen, Zhangyang Wang, Yingbin Liang
TranSegPGD: Improving Transferability of Adversarial Examples on Semantic Segmentation
Xiaojun Jia, Jindong Gu, Yihao Huang, Simeng Qin, Qing Guo, Yang Liu, Xiaochun Cao
Fool the Hydra: Adversarial Attacks against Multi-view Object Detection Systems
Bilel Tarchoun, Quazi Mishkatul Alam, Nael Abu-Ghazaleh, Ihsen Alouani
Improving the Robustness of Quantized Deep Neural Networks to White-Box Attacks using Stochastic Quantization and Information-Theoretic Ensemble Training
Saurabh Farkya, Aswin Raghavan, Avi Ziskind
On the Adversarial Robustness of Graph Contrastive Learning Methods
Filippo Guerranti, Zinuo Yi, Anna Starovoit, Rafiq Kamel, Simon Geisler, Stephan Günnemann
SenTest: Evaluating Robustness of Sentence Encoders
Tanmay Chavan, Shantanu Patankar, Aditya Kane, Omkar Gokhale, Geetanjali Kale, Raviraj Joshi
Quantum Neural Networks under Depolarization Noise: Exploring White-Box Attacks and Defenses
David Winderl, Nicola Franco, Jeanette Miriam Lorenz
GSE: Group-wise Sparse and Explainable Adversarial Attacks
Shpresim Sadiku, Moritz Wagner, Sebastian Pokutta