An Image Splicing and Copy-Move Detection Method Based on Convolutional Neural Networks with Global Average Pooling

Splicing and copy-move are two well-known methods of image tampering, while detection of image splicing and copy-move forgery is an important research topic in image forensics. In this paper, a method based on convolutional neural network with global average pooling was proposed for splicing and copy-move tampering detection. To detect image tampering, the inconsistency between the authentic images and the tampered images should be captured regardless of the image contents. So, the existing strategy using high-pass filter in SRM as initialization of the first layer was improved to reduce the influence of image content and make the features more diverse on each channel at the same time. In order to reduce the number of parameters in the fully connected layers and avoid overfitting, global average pooling was utilized before fully connected layers in the proposed model. Experiments on three public image tampering datasets demonstrated that the proposed method outperformed some state-of-the-art methods.