Image resampling detection is an important task in the field of image forensic.The purpose is to detect whether the image is resampled.Current methods based on deep learning are mostly aimed at fixed resampling factors.However,they rarely consider the case that the resampling factors are completely random. In this paper,according to the principle of interpolation involved in resampling operation,an efficient preprocessing structure is designed to avoid the interference of image content.Then resampling features are extracted and screened by deformable convolutional layer and efficient channel attention mechanism respectively,so as to effectively improve the performance of convolutional neural network in extracting resampling features with different resampling factors.The experimental results show that whether for uncompressed resampling images or resampling images after JPEG compression, the method can detect effectively,and the prediction accuracy is greatly improved compared with the current methods.