Double JPEG compression detection for distinguishable blocks in images compressed with same quantization matrix

Detection of compression history is a crucial step in verifying the authenticity of a JPEG image. Previous approaches for double compression detection with the same quantization matrix are designed for full-sized images or large patches. In this paper, we propose a novel deep learning based approach that utilizes spatial and frequency domain information from the error blocks obtained from multiple compression stages and uses a multi-column CNN architecture to classify distinguishable blocks of size 8×8. Three successive error blocks are obtained from the given JPEG block and its repeated compression by taking the difference between inverse discrete cosine transform (DCT) of de-quantized DCT coefficients and the reconstructed blocks. On average, the performance gain of the proposed approach over the baseline method in terms of TPR, TNR, and balanced accuracy is 4.04%, 1.6%, and 2.8%, respectively. We also show the applicability of the method for unseen quality factors.