Backdoor attack has emerged as a major security threat to deep neural networks (DNNs).
While existing defense methods have demonstrated promising results on detecting or …

Deep neural networks (DNNs) are known vulnerable to backdoor attacks, a training time
attack that injects a trigger pattern into a small proportion of training data so as to control the …

Backdoor injection attack is an emerging threat to the security of neural networks, however,
there still exist limited effective defense methods against the attack. In this paper, we …

Deep neural networks (DNNs) are recently shown to be vulnerable to backdoor attacks,
where attackers embed hidden backdoors in the DNN model by injecting a few poisoned …

Machine learning models have been widely adopted in several fields. However, most recent
studies have shown several vulnerabilities from attacks with a potential to jeopardize the …

Reinforcement Learning with Human Feedback (RLHF) is a methodology designed to align
Large Language Models (LLMs) with human preferences, playing an important role in LLMs …

Despite of its tremendous popularity and success in computer vision (CV) and natural
language processing, deep learning is inherently vulnerable to adversarial attacks in which …

Reinforcement Learning (RL) has achieved a plenty of breakthroughs in the past decade.
Notably, existing studies have shown that RL is suffered from poisoning attack, which results …

Crowd counting is a regression task that estimates the number of people in a scene image,
which plays a vital role in a range of safety-critical applications, such as video surveillance …

Deep neural networks are susceptible to poisoning attacks by purposely polluted training
data with specific triggers. As existing episodes mainly focused on attack success rate with …