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

Multimodal contrastive pretraining has been used to train multimodal representation models,
such as CLIP, on large amounts of paired image-text data. However, previous studies have …

Self-supervised learning is widely used in various domains for building foundation models. It
has been demonstrated to achieve state-of-the-art performance in a range of tasks. In the …

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

Object detection models are vulnerable to backdoor or trojan attacks, where an attacker can
inject malicious triggers into the model, leading to altered behavior during inference. As a …

Self-supervised learning (SSL) is a prevalent approach for encoding data representations.
Using a pre-trained SSL image encoder and subsequently training a downstream classifier …

Adversarial phenomenon has been widely observed in machine learning (ML) systems,
especially in those using deep neural networks, describing that ML systems may produce …

Backdoor attack poses a significant security threat to Deep Learning applications. Existing
attacks are often not evasive to established backdoor detection techniques. This …

As AI systems become more capable, widely deployed, and increasingly autonomous in
critical areas such as cybersecurity, biological research, and healthcare, ensuring their …

AI Safety is an emerging area of critical importance to the safe adoption and deployment of
AI systems. With the rapid proliferation of AI and especially with the recent advancement of …