# Non-Semantic Evaluation of Image Forensics Tools: Methodology and Database

arXiv: https://arxiv.org/abs/2105.02700 · HTML: https://mlsanity.com/paper/2105.02700/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2021.

## Built on by (most cited first, 2 of 2)

- [TrainFors: A Large Benchmark Training Dataset for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2308.05264/) (2023)
- [Enhanced Wavelet Scattering Network for image inpainting detection](https://mlsanity.com/paper/2409.17023/) (2024)

## Abstract (arXiv)

With the aim of evaluating image forensics tools, we propose a methodology to create forgeries traces, leaving intact the semantics of the image. Thus, the only forgery cues left are the specific alterations of one or several aspects of the image formation pipeline. This methodology creates automatically forged images that are challenging to detect for forensic tools and overcomes the problem of creating convincing semantic forgeries. Based on this methodology, we create the Trace database and conduct an evaluation of the main state-of-the-art image forensics tools.