Copy-move forgery detection based on convolutional kernel network

Conventional copy-move forgery detection methods mostly make use of hand-crafted features to conduct feature extraction and patch matching. However, the discriminative capability and the invariance to particular transformations of hand-crafted features are not good enough, which imposes restrictions on the performance of copy-move forgery detection. To solve this problem, we propose to utilize Convolutional Kernel Network to conduct copy-move forgery detection. Convolutional Kernel Network is a kind of data-driven local descriptor with the deep convolutional architecture. It can achieve competitive performance for its excellent discriminative capability. To well adapt to the condition of copy-move forgery detection, three significant improvements are made: First of all, our Convolutional Kernel Network is reconstructed for GPU. The GPU-based reconstruction results in high efficiency and makes it possible to apply to thousands of patches matching in copy-move forgery detection. Second, a segmentation-based keypoint distribution strategy is proposed to generate homogeneous distributed keypoints. Last but not least, an adaptive oversegmentation method is adopted. Experiments on the publicly available datasets are conducted to testify the state-of-the-art performance of the proposed method.