In recent years, deep neural networks (DNNs) have achieved widespread success in computer vision tasks, including face recognition, autonomous driving, and medical diagnosis. However, their vulnerability to adversarial attacks has also raised serious concerns regarding system security and reliability. This paper provides a comprehensive overview of recent advances in adversarial defense methods in computer vision. It explores defense strategies for Large Vision-Language Models (LVLMs) while covering traditional adversarial defense methods. The article first explains the basic concepts of adversarial samples, their generation principles, and their actual threats in white-box, black-box and physical worlds. It then systematically combs through the multi-dimensional defense strategies based on model architecture design, dynamic adversarial training, and input-output space purification. Meanwhile, for the security challenges of LVLM in the era of significant models, two types of strategies, input-output space defense and dynamic training defense, are discussed. Finally, this paper summarizes the key issues facing adversarial defense and provides an outlook on future research directions.

MOTIVATION: Multilayer perceptrons (MLP) represent one of the widely used and effective machine learning methods currently applied to diagnostic classification based on high-dimensional genomic data. Since the dimensionalities of the existing genomic data often exceed the available sample sizes by orders of magnitude, the MLP performance may degrade owing to the curse of dimensionality and over-fitting, and may not provide acceptable prediction accuracy. RESULTS: Based on Fisher linear discriminant analysis, we designed and implemented an MLP optimization scheme for a two-layer MLP that effectively optimizes the initialization of MLP parameters and MLP architecture. The optimized MLP consistently demonstrated its ability in easing the curse of dimensionality in large microarray datasets. In comparison with a conventional MLP using random initialization, we obtained significant improvements in major performance measures including Bayes classification accuracy, convergence properties and area under the receiver operating characteristic curve (A(z)). SUPPLEMENTARY INFORMATION: The Supplementary information is available on http://www.cbil.ece.vt.edu/publications.htm