Deep neural networks have played a crucial part in many critical domains such as
autonomous driving face recognition and medical diagnosis. However deep neural networks …

Adversarial attacks can compromise the robustness of real-world detection models.
However, evaluating these models under real-world conditions poses challenges due to …

Autonomous driving technology has advanced significantly with deep learning, but noise
and attacks threaten its real-world deployment. While research has revealed vulnerabilities …

Neural networks have increasingly influenced people's lives. Ensuring the faithful
deployment of neural networks as designed by their model owners is crucial, as they may be …

Lane detection (LD) is an essential component of autonomous driving systems, providing
fundamental functionalities like adaptive cruise control and automated lane centering …

Large Vision-Language Models (LVLMs) have been widely adopted in various applications;
however, they exhibit significant gender biases. Existing benchmarks primarily evaluate …

Quantization is a promising technique for reducing the bit-width of deep models to improve
their runtime performance and storage efficiency, and thus becomes a fundamental step for …

Federated learning presents massive potential for privacy-friendly collaboration. However,
the performance of federated learning is deeply affected by byzantine attacks, where …