面向电力场景的双通道图像拼接窜改检测模型

随着电力生产智能化的推进，电力图像被广泛应用。然而由于图像编辑软件的发展导致部分电力图像被恶意窜改，严重影响电力生产进程。其中以拼接窜改最为常见。基于深度学习技术，提出了一种双通道CenterNet的图像拼接窜改检测模型。原色图像通道提取窜改图像的色调、纹理等特征，隐写分析通道发掘图像窜改区域的噪声特征。同时设计了一种基于注意力机制的特征融合模块，自适应地对双通道的特征进行加权融合，以增强检测模型的特征识别能力。实验结果表明，所提模型可以达到更优的检测性能，在电力图像的窜改检测应用中具有实际意义。

Dual-channel image splicing forgery detection model of electric power site

Nowadays intelligent power production uses electric power images widely. However, due to the development of image editing software, some people maliciously tampere the power images, which seriously affected the power production process. In order to solve this problem, this paper proposed a dual-channel CenterNet model for power image splicing forgery detection. The color channel extracted the obvious visual features of the image. The steganalysis channel explored the noise feature. At the same time, the algorithm used a feature fusion module based on attention mechanism, it integrated the dual-channel features to enhanced the feature recognition ability. Experiments show that the model can get better detection performance and has practical significance in the forgery detection application of power images.