  Forged images have a ubiquitous presence in today's world due to ease of
availability of image manipulation tools. In this letter, we propose a deep
learning-based novel approach which utilizes the inherent relationship between
DCT coefficient histograms and corresponding quantization step sizes to
distinguish between original and forged regions in a JPEG image, based on the
detection of single and double compressed blocks, without fully decompressing
the JPEG image. We consider a diverse set of 1,120 quantization matrices
collected in a recent study as compared to standard 100 quantization matrices
for training, testing, and creating realistic forgeries. In particular, we
carefully design the input to DenseNet with a specific combination of
quantization step sizes and the respective histograms for a JPEG block. Using
this input to learn the compression artifacts produces state-of-the-art results
for the detection of single and double compressed blocks of sizes $256 \times
256$ and gives better results for smaller blocks of sizes $128 \times 128$ and
$64 \times 64$. Consequently, improved forgery localization performances are
obtained on realistic forged images. Also, in the case of test blocks
compressed with completely different quantization matrices as compared to
matrices used in training, the proposed method outperforms the current
state-of-the-art.
