# Aligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1708.00930 · HTML: https://mlsanity.com/paper/1708.00930/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2017.

## Built on by (most cited first, 22 of 22)

- [Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2108.12947/) (2021)
- [Towards Flexible Blind JPEG Artifacts Removal](https://mlsanity.com/paper/2109.14573/) (2021)
- [TransForensics: Image Forgery Localization with Dense Self-Attention](https://mlsanity.com/paper/2108.03871/) (2021)
- [Comprint: Image Forgery Detection and Localization using Compression Fingerprints](https://mlsanity.com/paper/2210.02227/) (2022)
- [Image Splicing Detection, Localization and Attribution via JPEG Primary Quantization Matrix Estimation and Clustering](https://mlsanity.com/paper/2102.01439/) (2021)
- [DepthFake: a depth-based strategy for detecting Deepfake videos](https://mlsanity.com/paper/2208.11074/) (2022)
- [How Do Deepfakes Move? Motion Magnification for Deepfake Source Detection](https://mlsanity.com/paper/2212.14033/) (2022)
- [Is JPEG AI going to change image forensics?](https://mlsanity.com/paper/2412.03261/) (2024)
- [Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation](https://mlsanity.com/paper/2506.05890/) (2025)
- [Attacking Image Splicing Detection and Localization Algorithms Using Synthetic Traces](https://mlsanity.com/paper/2211.12314/) (2022)
- [OAPT: Offset-Aware Partition Transformer for Double JPEG Artifacts Removal](https://mlsanity.com/paper/2408.11480/) (2024)
- [Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection](https://mlsanity.com/paper/2212.05667/) (2022)
- [Frame-rate Up-conversion Detection Based on Convolutional Neural Network for Learning Spatiotemporal Features](https://mlsanity.com/paper/2103.13674/) (2021)
- [Q-matrix Unaware Double JPEG Detection using DCT-Domain Deep BiLSTM Network](https://mlsanity.com/paper/2104.04765/) (2021)
- [Is It Certainly a Deepfake? Reliability Analysis in Detection & Generation Ecosystem](https://mlsanity.com/paper/2509.17550/) (2025)
- [DHNet: Double MPEG-4 Compression Detection via Multiple DCT Histograms](https://mlsanity.com/paper/2107.08939/) (2021)
- [Deep Metric Color Embeddings for Splicing Localization in Severely Degraded Images](https://mlsanity.com/paper/2206.10737/) (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)
- [Recompression Based JPEG Tamper Detection and Localization Using Deep Neural Network Eliminating Compression Factor Dependency](https://mlsanity.com/paper/2407.02942/) (2024)
- [Three Forensic Cues for JPEG AI Images](https://mlsanity.com/paper/2504.03191/) (2025)
- [SegWave: Wavelet-Driven Segmentation of Tampered Regions](https://mlsanity.com/paper/2608.30714/) (2026)

## Abstract (arXiv)

Due to the wide diffusion of JPEG coding standard, the image forensic community has devoted significant attention to the development of double JPEG (DJPEG) compression detectors through the years. The ability of detecting whether an image has been compressed twice provides paramount information toward image authenticity assessment. Given the trend recently gained by convolutional neural networks (CNN) in many computer vision tasks, in this paper we propose to use CNNs for aligned and non-aligned double JPEG compression detection. In particular, we explore the capability of CNNs to capture DJPEG artifacts directly from images. Results show that the proposed CNN-based detectors achieve good performance even with small size images (i.e., 64x64), outperforming state-of-the-art solutions, especially in the non-aligned case. Besides, good results are also achieved in the commonly-recognized challenging case in which the first quality factor is larger than the second one.