With the development and widespread application of digital image processing
technology, image splicing has become a common method of image manipulation,
raising numerous security and legal issues. This paper introduces a new
splicing image detection algorithm based on the statistical characteristics of
natural images, aimed at improving the accuracy and efficiency of splicing
image detection. By analyzing the limitations of traditional methods, we have
developed a detection framework that integrates advanced statistical analysis
techniques and machine learning methods. The algorithm has been validated using
multiple public datasets, showing high accuracy in detecting spliced edges and
locating tampered areas, as well as good robustness. Additionally, we explore
the potential applications and challenges faced by the algorithm in real-world
scenarios. This research not only provides an effective technological means for
the field of image tampering detection but also offers new ideas and methods
for future related research.

基于自然图像的统计特征，本文介绍了一种用于提高图像篡改检测精确度和效率的新的拼接图像检测算法，该算法通过整合先进的统计分析技术和机器学习方法构建了一个检测框架，并在多个公共数据集上进行了验证，显示出在检测拼接边缘和定位篡改区域方面具有高准确性和良好鲁棒性，此外，还探讨了算法在实际场景中的潜在应用和面临的挑战。此研究不仅为图像篡改检测领域提供了一种有效的技术手段，而且为未来相关研究提供了新的思路和方法。

基于自然图像统计特征的拼接图像检测算法研究

Research on Splicing Image Detection Algorithms Based on Natural Image  Statistical Characteristics

Wi-Fi fingerprinting has emerged as the most popular approach to indoor
localization. The use of ML algorithms has greatly improved the localization
performance of Wi-Fi fingerprinting, but its success depends on the
availability of fingerprint databases composed of a large number of RSSIs, the
MAC addresses of access points, and the other measurement information. However,
most fingerprint databases do not reflect well the time varying nature of
electromagnetic interferences in complicated modern indoor environment. This
could result in significant changes in statistical characteristics of
training/validation and testing datasets, which are often constructed at
different times, and even the characteristics of the testing datasets could be
different from those of the data submitted by users during the operation of
localization systems after their deployment. In this paper, we consider the
implications of time-varying Wi-Fi fingerprints on indoor localization from a
data-centric point of view and discuss the differences between static and
dynamic databases. As a case study, we have constructed a dynamic database
covering three floors of the IR building of XJTLU based on RSSI measurements,
over 44 days, and investigated the differences between static and dynamic
databases in terms of statistical characteristics and localization performance.
The analyses based on variance calculations and Isolation Forest show the
temporal shifts in RSSIs, which result in a noticeable trend of the increase in
the localization error of a Gaussian process regression model with the maximum
error of 6.65 m after 14 days of training without model adjustments. The
results of the case study with the XJTLU dynamic database clearly demonstrate
the limitations of static databases and the importance of the creation and
adoption of dynamic databases for future indoor localization research and
real-world deployment.

本文通过考虑时间变化的 Wi-Fi 指纹对室内定位的影响，从数据中心的角度讨论了静态和动态数据库之间的差异，并以浙江财经大学国际商学院大楼的动态数据库为案例，通过方差计算和孤立森林算法的分析，揭示了 RSSI 的时间漂移对高斯过程回归模型定位误差的增加趋势，进而明确了静态数据库的局限性和未来室内定位研究及实际部署中动态数据库的重要性。