论文标题
基于噪声和边缘的双分支图像操纵检测
Noise and Edge Based Dual Branch Image Manipulation Detection
论文作者
论文摘要
与普通的计算机视觉任务不同，将图像操纵检测任务更多地关注图像的语义内容，更多地关注图像操纵的微妙信息。在本文中，通过改进的约束卷积提取的噪声图像用作模型的输入，而不是原始图像，以获得更微妙的操纵痕迹。同时，由高分辨率分支和上下文分支组成的双分支网络被用来尽可能捕获伪像的痕迹。通常，大多数操纵将操纵伪像在操纵边缘上。专门设计的操纵边缘检测模块是基于双分支网络构建的，以更好地识别这些伪像。图像中像素之间的相关性与它们的距离密切相关。两个像素越远，相关性越弱。我们为自我发场模块添加了一个距离因子，以更好地描述像素之间的相关性。四个公开可用图像操作数据集的实验结果证明了我们模型的有效性。
Unlike ordinary computer vision tasks that focus more on the semantic content of images, the image manipulation detection task pays more attention to the subtle information of image manipulation. In this paper, the noise image extracted by the improved constrained convolution is used as the input of the model instead of the original image to obtain more subtle traces of manipulation. Meanwhile, the dual-branch network, consisting of a high-resolution branch and a context branch, is used to capture the traces of artifacts as much as possible. In general, most manipulation leaves manipulation artifacts on the manipulation edge. A specially designed manipulation edge detection module is constructed based on the dual-branch network to identify these artifacts better. The correlation between pixels in an image is closely related to their distance. The farther the two pixels are, the weaker the correlation. We add a distance factor to the self-attention module to better describe the correlation between pixels. Experimental results on four publicly available image manipulation datasets demonstrate the effectiveness of our model.
