This work tackles an intriguing and fundamental open challenge in representation learning: Given a well-trained deep learning model, can it be reprogrammed to enhance its robustness against adversarial or noisy input perturbations without altering its parameters? To explore this, we revisit the core feature transformation mechanism in representation learning and propose a novel non-linear robust pattern matching technique as a robust alternative. Furthermore, we introduce three model reprogramming paradigms to offer flexible control of robustness under different efficiency requirements. Comprehensive experiments and ablation studies across diverse learning models ranging from basic linear model and MLPs to shallow and modern deep ConvNets demonstrate the effectiveness of our approaches. This work not only opens a promising and orthogonal direction for improving adversarial defenses in deep learning beyond existing methods but also provides new insights into designing more resilient AI systems with robust statistics.

本研究解决了表征学习中的一个重要挑战，即在不改变参数的情况下，如何重编程已训练好的深度学习模型以增强其对对抗性或噪声输入的鲁棒性。提出了一种新的非线性鲁棒模式匹配技术，并引入三种模型再编程范式，以适应不同的鲁棒性需求。这项工作的实验结果显示了其在提升深度学习对抗性防御和设计更具弹性的AI系统方面的潜在影响。

表征学习中的鲁棒性再编程