Tampering with joint photographic experts group (JPEG) images often produces double JPEG (DJPEG) compression traces,and analyzing the traces can help reveal the image compression history and enable tampering region localization.Existing algorithms perform poorly when the image size is small and the quality factor (QF) is low, and there are restrictions on the combination of the two QFs.In this paper,an end-to-end mixed QF DJPEG compressed image forensics network named DJPEGNet is proposed.First,the preprocessing layer is used to extract the quantization table (Qtable) features representing the compression history information from the image header file,and the image is converted from the spatial domain to the discrete cosine transform (DCT) domain to construct statistical histogram features.Then,the two features are input into the main structure formed by stacking the depthwise separable convolution and residual structure,and the binary classification result is output.Finally,a sliding window algorithm is used to automatically locate the tampered region and draw a probability distribution map.The experimental results show that, on small-size datasets generated by different Qtable sets,DJPEGNet outperforms the existing state-of-the-art algorithms in all indicators,with ACC increased by 1.78%,TPR increased by 2.00%,TNR increased by 1.60%.