Deep Learning is currently used to perform multiple tasks, such as object recognition, face
recognition, and natural language processing. However, Deep Neural Networks (DNNs) are …

Although backdoor learning is an active research topic in the NLP domain, the literature
lacks studies that systematically categorize and summarize backdoor attacks and defenses …

Adversarial attacks in deep learning models, especially for safety-critical systems, are
gaining more and more attention in recent years, due to the lack of trust in the security and …

Adversarial patch attacks are an emerging security threat for real world deep learning
applications. We present Demasked Smoothing, the first approach (up to our knowledge) to …

Patch attack, which introduces a perceptible but localized change to the input image, has
gained significant momentum in recent years. In this paper, we present a unified framework …

The Transformers architecture has recently emerged as a revolutionary paradigm in the field
of deep learning, particularly excelling in Natural Language Processing (NLP) and …

The attention mechanism has been proven effective on various visual tasks in recent years.
In the semantic segmentation task, the attention mechanism is applied in various methods …

Convolutional Neural Networks have become an integral part of anomaly detection in Cyber-
Physical Systems (CPS). Although highly accurate, the advent of adversarial patches …

The physical-world adversarial patch attack poses a security threat to AI perception models
in autonomous vehicles. To mitigate this threat, researchers have designed defenses with …

对抗攻击是指通过在原始输入中植入人眼无法察觉的微小扰动, 误导深度学习模型做出错误预测
的攻击. 与数字域对抗攻击相比, 物理域对抗攻击可实现对抗性输入被采集设备捕获并转换为 …