Deep Robust Vision Model Based On Adversarial Training

This study delves into deep robust visual models using adversarial training,with the objective of enhancing the robustness and security of computer vision systems.It starts by discussing prevalent research methods in adversarial training and domain generalization,highlighting that most existing research focuses on only one of these factors.However,both factors may coexist in real-world deep model tasks,necessitating their organic combination for a more general and practical model.To tackle this challenge,we propose a research method,(a)Generalized Robust Contrastive Learning(GRo CL),which is built upon a self-supervised contrastive learning framework to improve domain generalization tasks.This method elegantly incorporates domain bias considerations using advanced self-supervised adversarial training techniques,resulting in promising outcomes.Simultaneously,we introduce a novel defense perspective,(b)Uncertainty Decision Boundary Linear Evaluation(UDBLE),based on Uncertainty Classifier for passive defense,which focuses on altering decision boundaries to combat misleading attacks.Additionally,our study elegantly addresses domain shift issues by incorporating existing advanced self-supervised adversarial training methods,leading to favorable results.We also present a novel optimization approach centered on the dynamic uncertainty of decision boundaries,which not only yields positive outcomes but also offers significant research potential.Future work will explore the application of this method in other fields and seek improvements and optimizations through the integration of more advanced technologies.The innovative contributions of this study include:(1)tackling more complex tasks such as domain shift and adversarial samples simultaneously?(2)introducing a simple and easy-to-implement approach to address the issue of domain bias?(3)proposing a novel optimization strategy that leverages dynamic uncertainty of the decision boundary,which not only achieves promising results but also opens up new avenues for future research.