Lightweight Deep Learning Fusion Models for Image Forgery Detection

A Local Dynamic Neighborhood (LDN) method for outlier detection in large-scale datasets is presented in this study. In order to identify abnormalities based on dynamically specified neighborhoods, the suggested framework makes use of machine learning methods like Isolation Forest and k-Nearest Neighbors (k-NN). The LDN methodology improves detection accuracy in diverse data environments by adapting to local data density, in contrast to standard methods. Using a number of benchmark datasets, the framework is assessed and found to perform better at finding outliers with fewer false positives. Applications like network security, environmental monitoring, and fraud detection are especially well-suited for this approach.