The prosperity of machine learning has been accompanied by increasing attacks on the
training process. Among them, poisoning attacks have become an emerging threat during …

The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …

Adversarial examples are perturbed inputs designed to fool machine learning models.
Adversarial training injects such examples into training data to increase robustness. To …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

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

Deep neural networks are vulnerable to adversarial examples that mislead the models with
imperceptible perturbations. Though adversarial attacks have achieved incredible success …

Transferability of adversarial examples is of central importance for attacking an unknown
model, which facilitates adversarial attacks in more practical scenarios, eg, blackbox attacks …

Deep learning models are vulnerable to adversarial examples crafted by applying human-
imperceptible perturbations on benign inputs. However, under the black-box setting, most …

Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

For black-box attacks, the gap between the substitute model and the victim model is usually
large, which manifests as a weak attack performance. Motivated by the observation that the …