A novel deep learning framework for double JPEG compression detection of small size blocks

Abstract

Double JPEG compression detection plays a vital role in multimedia forensics, to find out whether a JPEG image is authentic or manipulated. However, it still remains to be a challenging task in the case when the quality factor of the first compression is much higher than that of the second compression, as well as in the case when the targeted image blocks are quite small. In this work, we present a novel end-to-end deep learning framework taking raw DCT coefficients as input to distinguish between single and double compressed images, which performs superior in the above two cases. Our proposed framework can be divided into two stages. In the first stage, we adopt an auxiliary DCT layer with sixty-four 8 × 8 DCT kernels. Using a specific layer to extract DCT coefficients instead of extracting them directly from JPEG bitstream allows our proposed framework to work even if the double compressed images are stored in spatial domain, e.g. in PGM, TIFF or other bitmap formats. The second stage is a deep neural network with multiple convolutional blocks to extract more effective features. We have conducted extensive experiments on three different image datasets. The experimental results demonstrate the superiority of our framework when compared with other state-of-the-art double JPEG compression detection methods either hand-crafted or learned using deep networks in the literature, especially in the two cases mentioned above. Furthermore, our proposed framework can detect triple and even multiple JPEG compressed images, which is scarce in the literature as far as we know.