Joint statistics matching for camera model identification of recompressed images.

Source camera identification has been well studied in laboratory environment where the training and test samples are all original images without recompression. However, image compression is quite common in the real world, when the training and test images are double JPEG compressed with different quantization tables, the identification accuracy of existing methods decreases dramati- cally. To address this challenge, we propose a novel iterative algorithm namely joint first and second order statistics matching (JSM) to learn a feature projection that projects the training and test fea- tures into a low dimensional subspace to reduce the shift caused by image recompression. Inspired by transfer learning, JSM aims to learn a new feature representation from original feature space by simultaneously matching the first and second order statistics between training and test features in a principled dimensionality reduction procedure. After the feature projection, the divergence between training and test features caused by recompression is reduced while the discriminative properties are preserved. Extensive experiments on public Dresden Image Database verify that JSM significantly outperforms several state-of-the-art methods on camera model identification of recompressed images.