The field of object detection and network security is rapidly evolving, with a focus on addressing class imbalance, improving detection performance, and enhancing privacy preservation. Researchers are exploring innovative approaches, such as exponentially weighted instance-aware repeat factor sampling and bi-grid reconstruction, to improve object detection in various scenarios, including long-tailed distributions and fine-grained anomaly detection. Meanwhile, network security is being enhanced through the development of privacy-preserving auditing schemes, multifractal IP address structure analysis, and state-space models for generating synthetic network traffic. Notably, papers such as Exponentially Weighted Instance-Aware Repeat Factor Sampling for Long-Tailed Object Detection and Bi-Grid Reconstruction for Image Anomaly Detection have introduced novel methods that significantly improve detection performance. Additionally, P2NIA: Privacy-Preserving Non-Iterative Auditing has proposed a mutually beneficial collaboration for both auditors and platforms, enhancing fairness assessments using synthetic or local data.