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

Wide-range and fine-grained vehicle detection plays a critical role in
enabling active safety features in intelligent driving systems. However,
existing vehicle detection methods based on rectangular bounding boxes (BBox)
often struggle with perceiving wide-range objects, especially small objects at
long distances. And BBox expression cannot provide detailed geometric shape and
pose information of vehicles. This paper proposes a novel wide-range Pseudo-3D
Vehicle Detection method based on images from a single camera and incorporates
efficient learning methods. This model takes a spliced image as input, which is
obtained by combining two sub-window images from a high-resolution image. This
image format maximizes the utilization of limited image resolution to retain
essential information about wide-range vehicle objects. To detect pseudo-3D
objects, our model adopts specifically designed detection heads. These heads
simultaneously output extended BBox and Side Projection Line (SPL)
representations, which capture vehicle shapes and poses, enabling
high-precision detection. To further enhance the performance of detection, a
joint constraint loss combining both the object box and SPL is designed during
model training, improving the efficiency, stability, and prediction accuracy of
the model. Experimental results on our self-built dataset demonstrate that our
model achieves favorable performance in wide-range pseudo-3D vehicle detection
across multiple evaluation metrics. Our demo video has been placed at
this https URL

这篇研究论文提出了一种基于单摄像头图像的广范围伪 3D 车辆检测方法，包括图像拼接、特殊设计的检测头以及联合约束损失，通过捕捉车辆的形状和姿态信息实现高精度的车辆检测。实验结果表明，在多项评估指标上，该模型在广范围伪 3D 车辆检测上表现出色。

利用单一摄像头进行高效宽范围伪 3D 车辆检测

An Efficient Wide-Range Pseudo-3D Vehicle Detection Using A Single  Camera

Image splicing is a very common image manipulation technique that is
sometimes used for malicious purposes. A splicing detec- tion and localization
algorithm usually takes an input image and produces a binary decision
indicating whether the input image has been manipulated, and also a
segmentation mask that corre- sponds to the spliced region. Most existing
splicing detection and localization pipelines suffer from two main
shortcomings: 1) they use handcrafted features that are not robust against
subsequent processing (e.g., compression), and 2) each stage of the pipeline is
usually optimized independently. In this paper we extend the formulation of the
underlying splicing problem to consider two input images, a query image and a
potential donor image. Here the task is to estimate the probability that the
donor image has been used to splice the query image, and obtain the splicing
masks for both the query and donor images. We introduce a novel deep
convolutional neural network architecture, called Deep Matching and Validation
Network (DMVN), which simultaneously localizes and detects image splicing. The
proposed approach does not depend on handcrafted features and uses raw input
images to create deep learned representations. Furthermore, the DMVN is
end-to-end op- timized to produce the probability estimates and the
segmentation masks. Our extensive experiments demonstrate that this approach
outperforms state-of-the-art splicing detection methods by a large margin in
terms of both AUC score and speed.

本文提出了一种名为 Deep Matching and Validation Network（DMVN）的新型深度卷积神经网络架构，该方法可以同时定位和检测图像拼接，不依赖于手工特征，并使用原始输入图像创建深度学习表示，通过端到端优化产生概率估计和分割掩模，通过广泛的实验表明，此方法在 AUC 得分和速度方面都大大优于现有的拼接检测方法。