Tampering region detection and localization of double JPEG com-pressed images
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 pa-per,an end-to-end mixed QF DJPEG compressed image forensics network named DJPEGNet is pro-posed.First,the preprocessing layer is used to extract the quantization table(Qtable)features represen-ting 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 fea-tures.Then,the two features are input into the main structure formed by stacking the depthwise separa-ble convolution and residual structure,and the binary classification result is output.Finally,a sliding win-dow 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％.
double JPEG(DJPEG)compressiontampering region localizationmixed quality factor(QF)image forensicssmall size
NETL
NSTL
万方数据
维普
