The prosperity of deep neural networks (DNNs) is largely benefited from open-source
datasets, based on which users can evaluate and improve their methods. In this paper, we …

In a backdoor attack, an adversary injects corrupted data into a model's training dataset in
order to gain control over its predictions on images with a specific attacker-defined trigger. A …

Abstract In recent decades, Generative Adversarial Network (GAN) and its variants have
achieved unprecedented success in image synthesis. However, well-trained GANs are …

Large language models (LLMs) have witnessed a meteoric rise in popularity among the
general public users over the past few months, facilitating diverse downstream tasks with …

Recent studies revealed that using third-party models may lead to backdoor threats, where
adversaries can maliciously manipulate model predictions based on backdoors implanted …

Saliency-based representation visualization (SRV)($ eg $, Grad-CAM) is one of the most
classical and widely adopted explainable artificial intelligence (XAI) methods for its simplicity …

Deep neural network (DNN) classifiers are potent instruments that can be used in various
security-sensitive applications. Nonetheless, they are vulnerable to certain attacks that …

Speech recognition systems driven by Deep Neural Networks (DNNs) have revolutionized
human-computer interaction through voice interfaces, which significantly facilitate our daily …

Deep neural networks (DNNs) have been widely and successfully adopted and deployed in
various applications of speech recognition. Recently, a few works revealed that these …

The proliferation of face forgery techniques has raised significant concerns within society,
thereby motivating the development of face forgery detection methods. These methods aim …