# Current Status and Trends in Image Anti-Forensics Research: A Bibliometric Analysis

arXiv: https://arxiv.org/abs/2408.11365 · HTML: https://mlsanity.com/paper/2408.11365/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 21 Aug 2024.

## Builds on (most cited first, 11 of 11)

- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues](https://mlsanity.com/paper/2007.09355/) (2020)
- [MesoNet: a Compact Facial Video Forgery Detection Network](https://mlsanity.com/paper/1809.00888/) (2018)
- [Detecting and Simulating Artifacts in GAN Fake Images](https://mlsanity.com/paper/1907.06515/) (2019)
- [Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial Examples](https://mlsanity.com/paper/2002.12749/) (2020)
- [Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input](https://mlsanity.com/paper/2203.09123/) (2022)
- [Exploring Adversarial Fake Images on Face Manifold](https://mlsanity.com/paper/2101.03272/) (2021)
- [Perception Matters: Exploring Imperceptible and Transferable Anti-forensics for GAN-generated Fake Face Imagery Detection](https://mlsanity.com/paper/2010.15886/) (2020)
- [Adversarial Attacks on Co-Occurrence Features for GAN Detection](https://mlsanity.com/paper/2009.07456/) (2020)
- [Making Generated Images Hard To Spot: A Transferable Attack On Synthetic Image Detectors](https://mlsanity.com/paper/2104.12069/) (2021)
- [Transformation on Computer-Generated Facial Image to Avoid Detection by Spoofing Detector](https://mlsanity.com/paper/1804.04418/) (2018)

## Abstract (arXiv)

Image anti-forensics is a critical topic in the field of image privacy and security research. With the increasing ease of manipulating or generating human faces in images, the potential misuse of such forged images is a growing concern. This study aims to comprehensively review the knowledge structure and research hotspots related to image anti-forensics by analyzing publications in the Web of Science Core Collection (WoSCC) database. The bibliometric analysis conducted using VOSViewer software has revealed the research trends, major research institutions, most influential publications, top publishing venues, and most active contributors in this field. This is the first comprehensive bibliometric study summarizing research trends and developments in image anti-forensics. The information highlights recent and primary research directions, serving as a reference for future research in image anti-forensics.