Native Robustness
Native robustness in machine learning focuses on developing models inherently resistant to various forms of input perturbations, including adversarial attacks and noisy data, without relying solely on post-hoc defenses. Current research emphasizes techniques like ensemble methods, reprogramming existing models, and modifying training procedures (e.g., using different learning rates for specific model layers or incorporating regularization methods) to improve robustness across diverse model architectures, including convolutional neural networks, vision transformers, and large language models. This field is crucial for deploying reliable AI systems in safety-critical applications, such as healthcare and autonomous driving, where model resilience to unexpected inputs is paramount.
Papers
Towards Evaluating the Robustness of Automatic Speech Recognition Systems via Audio Style Transfer
Weifei Jin, Yuxin Cao, Junjie Su, Qi Shen, Kai Ye, Derui Wang, Jie Hao, Ziyao Liu
Optimizing Sensor Network Design for Multiple Coverage
Lukas Taus, Yen-Hsi Richard Tsai
Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and Privacy
Feng Wang, M. Cenk Gursoy, Senem Velipasalar
RS-Reg: Probabilistic and Robust Certified Regression Through Randomized Smoothing
Aref Miri Rekavandi, Olga Ohrimenko, Benjamin I. P. Rubinstein
Self-supervised learning improves robustness of deep learning lung tumor segmentation to CT imaging differences
Jue Jiang, Aneesh Rangnekar, Harini Veeraraghavan
Can we Defend Against the Unknown? An Empirical Study About Threshold Selection for Neural Network Monitoring
Khoi Tran Dang, Kevin Delmas, Jérémie Guiochet, Joris Guérin
Certifying Robustness of Graph Convolutional Networks for Node Perturbation with Polyhedra Abstract Interpretation
Boqi Chen, Kristóf Marussy, Oszkár Semeráth, Gunter Mussbacher, Dániel Varró
Benchmarking Retrieval-Augmented Large Language Models in Biomedical NLP: Application, Robustness, and Self-Awareness
Mingchen Li, Zaifu Zhan, Han Yang, Yongkang Xiao, Jiatan Huang, Rui Zhang
An Empirical Study on the Robustness of Massively Multilingual Neural Machine Translation
Supryadi, Leiyu Pan, Deyi Xiong
Robustness of Decentralised Learning to Nodes and Data Disruption
Luigi Palmieri, Chiara Boldrini, Lorenzo Valerio, Andrea Passarella, Marco Conti, János Kertész
From Attack to Defense: Insights into Deep Learning Security Measures in Black-Box Settings
Firuz Juraev, Mohammed Abuhamad, Eric Chan-Tin, George K. Thiruvathukal, Tamer Abuhmed
Impact of Architectural Modifications on Deep Learning Adversarial Robustness
Firuz Juraev, Mohammed Abuhamad, Simon S. Woo, George K Thiruvathukal, Tamer Abuhmed