GRASP impacts the overall robustness of the model by specifically targeting the trigger region, reducing its effective radius and making it more sensitive to perturbations. Traditional augmentation techniques focus on enhancing the robustness of the entire input data through methods like flipping, translating, and adding noise uniformly across all training data. In contrast, GRASP enhances backdoor stealthiness by introducing controlled noise only to the trigger region in poisoned data points. This targeted approach sharpens the decision boundary around trigger-inserted inputs, ultimately reducing the trigger's effective radius without compromising the model's primary task performance.