# Camera-based Image Forgery Localization using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1808.09714 · HTML: https://mlsanity.com/paper/1808.09714/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2018.

## Built on by (most cited first, 4 of 4)

- [A Survey on Predicting the Factuality and the Bias of News Media](https://mlsanity.com/paper/2103.12506/) (2021)
- [Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection](https://mlsanity.com/paper/2212.05667/) (2022)
- [Detecting and Localizing Copy-Move and Image-Splicing Forgery](https://mlsanity.com/paper/2202.04069/) (2022)
- [Attack-Aware Deepfake Detection under Counter-Forensic Manipulations](https://mlsanity.com/paper/2512.22303/) (2025)

## Abstract (arXiv)

Camera fingerprints are precious tools for a number of image forensics tasks. A well-known example is the photo response non-uniformity (PRNU) noise pattern, a powerful device fingerprint. Here, to address the image forgery localization problem, we rely on noiseprint, a recently proposed CNN-based camera model fingerprint. The CNN is trained to minimize the distance between same-model patches, and maximize the distance otherwise. As a result, the noiseprint accounts for model-related artifacts just like the PRNU accounts for device-related non-uniformities. However, unlike the PRNU, it is only mildly affected by residuals of high-level scene content. The experiments show that the proposed noiseprint-based forgery localization method improves over the PRNU-based reference.