Large vision-language models (VLMs) such as GPT-4 have achieved unprecedented
performance in response generation, especially with visual inputs, enabling more creative …

Benefiting from the rapid development of deep learning, 2D and 3D computer vision
applications are deployed in many safe-critical systems, such as autopilot and identity …

Unrestricted adversarial attacks typically manipulate the semantic content of an image (eg,
color or texture) to create adversarial examples that are both effective and photorealistic …

Adversarial examples mislead deep neural networks with imperceptible perturbations and
have brought significant threats to deep learning. An important aspect is their transferability …

This paper introduces the system of game-theoretic interactions, which connects both the
explanation of knowledge encoded in a deep neural networks (DNN) and the explanation of …

Abstract Vision-Language Pre-training (VLP) models have achieved remarkable success in
practice, while easily being misled by adversarial attack. Though harmful, adversarial …

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 …

Intermediate-level attacks that attempt to perturb feature representations following an
adversarial direction drastically have shown favorable performance in crafting transferable …

Deep neural networks (DNNs) are vulnerable to adversarial attacks, which lead to incorrect
predictions. In black-box settings, transfer attacks can be conveniently used to generate …

Transfer-based attacks are a practical method of black-box adversarial attacks, in which the
attacker aims to craft adversarial examples from a source (surrogate) model that is …