Single image dehazing is a challenging vision task that recovers haze-free images from
observed hazy images. Recently, numerous learning-based dehazing methods have been …

The current advancements in image processing have led to significant progress in
polarization defogging methods. However, most existing approaches are not suitable for …

As computer networks become increasingly important in various domains, the need for
secure and reliable networks becomes more pressing, particularly in the context of …

Federated learning is a distributed learning method used to solve data silos and privacy
protection in machine learning, aiming to train global models together via multiple clients …

Federated Learning (FL) is a decentralized learning paradigm, enabling parties to
collaboratively train models while keeping their data confidential. Within autonomous …

Current research shows that the privacy of FL is threatened by an honest-but-curious server.
However, existing research focus on privacy attacks against the malicious server while …

The out-of-distribution (OOD) detection task assumes samples that follow the distribution of
training data as in-distribution (ID), while samples from other data distributions are …

Data poisoning attacks, where adversaries manipulate training data to degrade model
performance, are an emerging threat as machine learning becomes widely deployed in …

Artificial intelligence (AI) technology promotes human civilization, while it also raises
concerns, mainly related to security. AI plays a double-edged sword role in the network …