# CNN-Based Detection of Generic Constrast Adjustment with JPEG Post-processing

arXiv: https://arxiv.org/abs/1805.11318 · HTML: https://mlsanity.com/paper/1805.11318/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2018.

## Built on by (most cited first, 8 of 8)

- [Demystifying the Transferability of Adversarial Attacks in Computer Networks](https://mlsanity.com/paper/2110.04488/) (2021)
- [Resisting Deep Learning Models Against Adversarial Attack Transferability via Feature Randomization](https://mlsanity.com/paper/2209.04930/) (2022)
- [Do Not Deceive Your Employer with a Virtual Background: A Video Conferencing Manipulation-Detection System](https://mlsanity.com/paper/2106.15130/) (2021)
- [Employing Deep Ensemble Learning for Improving the Security of Computer Networks against Adversarial Attacks](https://mlsanity.com/paper/2209.12195/) (2022)
- [Spritz-PS: Validation of Synthetic Face Images Using a Large Dataset of Printed Documents](https://mlsanity.com/paper/2304.02982/) (2023)
- [A Transferable Anti-Forensic Attack on Forensic CNNs Using A Generative Adversarial Network](https://mlsanity.com/paper/2101.09568/) (2021)
- [Real or Virtual: A Video Conferencing Background Manipulation-Detection System](https://mlsanity.com/paper/2204.11853/) (2022)
- [TMFNet: Two-Stream Multi-Channels Fusion Networks for Color Image Operation Chain Detection](https://mlsanity.com/paper/2409.07701/) (2024)

## Abstract (arXiv)

Detection of contrast adjustments in the presence of JPEG postprocessing is known to be a challenging task. JPEG post processing is often applied innocently, as JPEG is the most common image format, or it may correspond to a laundering attack, when it is purposely applied to erase the traces of manipulation. In this paper, we propose a CNN-based detector for generic contrast adjustment, which is robust to JPEG compression. The proposed system relies on a patch-based Convolutional Neural Network (CNN), trained to distinguish pristine images from contrast adjusted images, for some selected adjustment operators of different nature. Robustness to JPEG compression is achieved by training the CNN with JPEG examples, compressed over a range of Quality Factors (QFs). Experimental results show that the detector works very well and scales well with respect to the adjustment type, yielding very good performance under a large variety of unseen tonal adjustments.