# Block-level Double JPEG Compression Detection for Image Forgery Localization

arXiv: https://arxiv.org/abs/2003.09393 · HTML: https://mlsanity.com/paper/2003.09393/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2020.

## Built on by (most cited first, 5 of 5)

- [Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2108.12947/) (2021)
- [GCA-Net : Utilizing Gated Context Attention for Improving Image Forgery Localization and Detection](https://mlsanity.com/paper/2112.04298/) (2021)
- [Frame-rate Up-conversion Detection Based on Convolutional Neural Network for Learning Spatiotemporal Features](https://mlsanity.com/paper/2103.13674/) (2021)
- [Three Forensic Cues for JPEG AI Images](https://mlsanity.com/paper/2504.03191/) (2025)
- [Tamper-evident Image using JPEG Fixed Points](https://mlsanity.com/paper/2504.17594/) (2025)

## Abstract (arXiv)

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 × 256 and gives better results for smaller blocks of sizes 128 × 128 and 64 × 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.