Image Backdoor Defense Algorithm Based on Deep Clustering Optimization

Currently, deep learning has widely penetrated into daily life, and as an extremely complex emerging field, deep learning inevitably faces various potential hacking threats. In this paper, an image backdoor defense algorithm based on deep clustering optimization is proposed, which uses clustering technology to accurately separate potential triggers from normal data, thus greatly improving the security and robustness of the deep learning model. First, this paper deeply analyzes the principle of the backdoor attack model and constructs its process to reveal its potential threat points. Subsequently, the classical deep clustering backdoor defense algorithm is reviewed and innovatively expanded on its basis. In the optimization process, two algorithms, ICA (Independent Component Analysis) and LDA (Linear Discriminant Analysis), are introduced in this paper. Through experiments, it is found that LDA can identify and defend backdoor attacks more effectively by virtue of its unique statistical characteristics and powerful classification ability, ensuring the stable operation and data security of deep learning systems.