面向特征融合的图像多窜改检测与定位算法

现有的图像窜改检测方法大多只针对某一种窜改方式，且存在窜改区域边界检测精度不高的问题，对此，提出了一种基于U型网络的双流编码器—解码器架构的图像窜改检测方法。首先利用编码器与解码器之间跳跃连接的方式来融合窜改图像中的低级和高级特征，并使用空洞卷积和CBAM注意力机制对编码器输出的特征进行融合，使得网络对不同尺度大小的窜改区域都有较好的定位性能；其次为了提高网络对窜改区域的边界检测精度，使用图像形态学方法制作了窜改边界数据集；最后使用多损失函数来同时优化网络的性能，即采用交叉熵和均方根损失函数来分别度量预测图的窜改区域损失和窜改边界损失。在CASIA、Columbia、NIST16、Coverage四个公开数据集上的实验结果表明，所提方法可以有效地检测出拼接和复制—粘贴两种窜改方式所伪造图像的窜改区域，输出像素级别的窜改区域定位图，且与其他主流窜改检测方法相比，所提方法在CASIA和Columbia数据集上的AUC值达到最高，在Columbia数据集上的F1值达到最高。

Feature fusion-oriented image multi-tampering detection and localization algorithm

Most of the existing image tampering detection methods are only for a certain tampering method, and there is a problem that the detection accuracy of the tampered area boundary is not high. In this regard, this paper proposed a U-shaped network based on dual-stream encoder-decoder architecture for image tampering. Firstly, the method used the skip connection between the encoder and the decoder to fuse the low-level and high-level features in the tampered image, and used the atrous convolution and CBAM attention mechanism to fuse the features output by the encoder, so that the network had better localization performance for tampered regions of different scales. Secondly, in order to improve the network''s detection accuracy of the boundary of the tampered area, this algorithm used the image morphological method to make a tampered boundary dataset. Finally, it used multiple loss functions to optimize the performance of the network simultaneously, namely it used cross-entropy and root-mean-square loss functions to measure the tampered region loss and tampered boundary loss of the prediction graph respectively. The experimental results on four public datasets: CASIA, Columbia, NIST16, Coverage show that the proposed method can effectively detect the tampering area of the fake image with splicing and copy-paste tampering methods, and output pixel-level tampering region location map. Compared with other mainstream tampering detection methods, the proposed method achieves the highest AUC values on the CASIA and Columbia dataset and the highest F1 value on the Columbia dataset.