# Localization of JPEG double compression through multi-domain convolutional neural networks

arXiv: https://arxiv.org/abs/1706.01788 · HTML: https://mlsanity.com/paper/1706.01788/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2017.

## Built on by (most cited first, 15 of 15)

- [PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2103.10596/) (2021)
- [TransForensics: Image Forgery Localization with Dense Self-Attention](https://mlsanity.com/paper/2108.03871/) (2021)
- [CFL-Net: Image Forgery Localization Using Contrastive Learning](https://mlsanity.com/paper/2210.02182/) (2022)
- [TrainFors: A Large Benchmark Training Dataset for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2308.05264/) (2023)
- [Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection](https://mlsanity.com/paper/2212.05667/) (2022)
- [Q-matrix Unaware Double JPEG Detection using DCT-Domain Deep BiLSTM Network](https://mlsanity.com/paper/2104.04765/) (2021)
- [A Visually Attentive Splice Localization Network with Multi-Domain Feature Extractor and Multi-Receptive Field Upsampler](https://mlsanity.com/paper/2401.06995/) (2024)
- [Towards Effective Image Forensics via A Novel Computationally Efficient Framework and A New Image Splice Dataset](https://mlsanity.com/paper/2401.06998/) (2024)
- [GIID-Net: Generalizable Image Inpainting Detection via Neural Architecture Search and Attention](https://mlsanity.com/paper/2101.07419/) (2021)
- [Detecting and Segmenting Adversarial Graphics Patterns from Images](https://mlsanity.com/paper/2108.09383/) (2021)
- [Deep Metric Color Embeddings for Splicing Localization in Severely Degraded Images](https://mlsanity.com/paper/2206.10737/) (2022)
- [Learning Double-Compression Video Fingerprints Left from Social-Media Platforms](https://mlsanity.com/paper/2212.03658/) (2022)
- [Datasets, Clues and State-of-the-Arts for Multimedia Forensics: An Extensive Review](https://mlsanity.com/paper/2401.06999/) (2024)
- [On the exploitation of DCT statistics for cropping detectors](https://mlsanity.com/paper/2403.14789/) (2024)
- [TMFNet: Two-Stream Multi-Channels Fusion Networks for Color Image Operation Chain Detection](https://mlsanity.com/paper/2409.07701/) (2024)

## Abstract (arXiv)

When an attacker wants to falsify an image, in most of cases she/he will perform a JPEG recompression. Different techniques have been developed based on diverse theoretical assumptions but very effective solutions have not been developed yet. Recently, machine learning based approaches have been started to appear in the field of image forensics to solve diverse tasks such as acquisition source identification and forgery detection. In this last case, the aim ahead would be to get a trained neural network able, given a to-be-checked image, to reliably localize the forged areas. With this in mind, our paper proposes a step forward in this direction by analyzing how a single or double JPEG compression can be revealed and localized using convolutional neural networks (CNNs). Different kinds of input to the CNN have been taken into consideration, and various experiments have been carried out trying also to evidence potential issues to be further investigated.