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 8x8. 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.