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 …

Image attribution algorithms aim to identify important regions that are highly relevant to
model decisions. Although existing attribution solutions can effectively assign importance to …

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

We propose an approach for adversarial attacks on dense prediction models (such as object
detectors and segmentation). It is well known that the attacks generated by a single …

Face recognition technology is increasingly being integrated into our daily life, eg Face ID.
With the advancement of machine learning algorithms, the personal information such as …

The field of few-shot learning (FSL) has shown promising results in scenarios where training
data is limited, but its vulnerability to backdoor attacks remains largely unexplored. We first …

Autoregressive Visual Language Models (VLMs) showcase impressive few-shot learning
capabilities in a multimodal context. Recently, multimodal instruction tuning has been …

Adversarial training has shown promise in building robust models against adversarial
examples. A major drawback of adversarial training is the computational overhead …

Despite the broad application of Machine Learning models as a Service (MLaaS), they are
vulnerable to model stealing attacks. These attacks can replicate the model functionality by …

Billions of people are sharing their daily live images on social media everyday. However,
malicious collectors use deep face recognition systems to easily steal their biometric …