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 - Page 42
FEMDA: Une m\'ethode de classification robuste et flexible
Grad-FEC: Unequal Loss Protection of Deep Features in Collaborative Intelligence
Interpretable Computer Vision Models through Adversarial Training: Unveiling the Robustness-Interpretability Connection
Analyzing the vulnerabilities in SplitFed Learning: Assessing the robustness against Data Poisoning Attacks