The emergence of Deep Neural Networks (DNNs) has revolutionized various domains,
enabling the resolution of complex tasks spanning image recognition, natural language …

The adversarial vulnerability of deep neural networks (DNNs) has drawn great attention due
to the security risk of applying these models in real-world applications. Based on …

Large text-to-image models have shown remarkable performance in synthesizing high-
quality images. In particular, the subject-driven model makes it possible to personalize the …

Large Language Models (LLMs) have led to significant improvements in many tasks across
various domains, such as code interpretation, response generation, and ambiguity handling …

Skip connection is an essential ingredient for modern deep models to be deeper and more
powerful. Despite their huge success in normal scenarios (state-of-the-art classification …

Malicious use of deepfakes leads to serious public concerns and reduces people's trust in
digital media. Although effective deepfake detectors have been proposed, they are …

Numerous works study black-box attacks on image classifiers, where adversaries generate
adversarial examples against unknown target models without having access to their internal …

The main challenge in deceiving face recognition (FR) models lies in the target model under
the black-box setting. Existing works seek to generate adversarial examples to improve the …

Adversarial attacks,\ie generating adversarial perturbations with a small magnitude to
deceive deep neural networks, are important for investigating and improving model …

Deep neural networks (DNNs) are vulnerable to adversarial examples. And, the adversarial
examples have transferability, which means that an adversarial example for a DNN model …