# Camera Trace Erasing

arXiv: https://arxiv.org/abs/2003.06951 · HTML: https://mlsanity.com/paper/2003.06951/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2020.

## Built on by (most cited first, 7 of 7)

- [PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2103.10596/) (2021)
- [Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images](https://mlsanity.com/paper/2106.07873/) (2021)
- [ProMark: Proactive Diffusion Watermarking for Causal Attribution](https://mlsanity.com/paper/2403.09914/) (2024)
- [Toward DNN of LUTs: Learning Efficient Image Restoration with Multiple Look-Up Tables](https://mlsanity.com/paper/2303.14506/) (2023)
- [Diffusion models meet image counter-forensics](https://mlsanity.com/paper/2311.13629/) (2023)
- [SISL:Self-Supervised Image Signature Learning for Splicing Detection and Localization](https://mlsanity.com/paper/2203.07824/) (2022)
- [Deep Image Restoration For Image Anti-Forensics](https://mlsanity.com/paper/2405.02751/) (2024)

## Abstract (arXiv)

Camera trace is a unique noise produced in digital imaging process. Most existing forensic methods analyze camera trace to identify image origins. In this paper, we address a new low-level vision problem, camera trace erasing, to reveal the weakness of trace-based forensic methods. A comprehensive investigation on existing anti-forensic methods reveals that it is non-trivial to effectively erase camera trace while avoiding the destruction of content signal. To reconcile these two demands, we propose Siamese Trace Erasing (SiamTE), in which a novel hybrid loss is designed on the basis of Siamese architecture for network training. Specifically, we propose embedded similarity, truncated fidelity, and cross identity to form the hybrid loss. Compared with existing anti-forensic methods, SiamTE has a clear advantage for camera trace erasing, which is demonstrated in three representative tasks. Code and dataset are available at https://github.com/ngchc/CameraTE.