Poisoning-based backdoor attacks are serious threat for training deep models on data from
untrustworthy sources. Given a backdoored model, we observe that the feature …

Backdoor defenses have been studied to alleviate the threat of deep neural networks
(DNNs) being backdoor attacked and thus maliciously altered. Since DNNs usually adopt …

Recent studies have demonstrated the susceptibility of deep neural networks to backdoor
attacks. Given a backdoored model, its prediction of a poisoned sample with trigger will be …

Backdoor attacks are serious security threats to machine learning models where an
adversary can inject poisoned samples into the training set, causing a backdoored model …

Textual backdoor attacks are a kind of practical threat to NLP systems. By injecting a
backdoor in the training phase, the adversary could control model predictions via predefined …

External audits of AI systems are increasingly recognized as a key mechanism for AI
governance. The effectiveness of an audit, however, depends on the degree of access …

While existing backdoor attacks have successfully infected multimodal contrastive learning
models such as CLIP they can be easily countered by specialized backdoor defenses for …

Backdoor defense, which aims to detect or mitigate the effect of malicious triggers introduced
by attackers, is becoming increasingly critical for machine learning security and integrity …

Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger
samples during the inference stage, ie, the test-time trigger sample detection, can prevent …

Recently, ChatGPT has gained significant attention in research due to its ability to interact
with humans effectively. The core idea behind this model is reinforcement learning (RL) fine …