Code models have made significant advancements in code intelligence by encoding
knowledge about programming languages. While previous studies have explored the …

Abstract Large pre-trained Vision-Language Models (VLMs) like CLIP despite having
remarkable generalization ability are highly vulnerable to adversarial examples. This work …

While prior research has proposed a plethora of methods that build neural classifiers robust
against adversarial robustness, practitioners are still reluctant to adopt them due to their …

Deep neural networks are vulnerable to adversarial examples. Adversarial training (AT) is
an effective defense against adversarial examples. However, AT is prone to overfitting which …

Adversarial robustness often comes at the cost of degraded accuracy, impeding the real-life
application of robust classification models. Training-based solutions for better trade-offs are …

Although deep neural networks (DNNs) have demonstrated remarkable capabilities, they
are vulnerable to adversarial examples. Adversarial examples are input data perturbed by …