mlsanityData to 7 Oct 2026

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

arXiv 2408.11365 ↗First posted 21 Aug 2024cs.CVcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 28 references that are in the map. First posted on arXiv 21 Aug 2024.

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11 of 28 references.

References · 11

  1. 2018Multimodal Unsupervised Image-to-Image Translationcited by 795
  2. 2020Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Cluescited by 518
  3. 2018MesoNet: a Compact Facial Video Forgery Detection Networkcited by 409
  4. 2019Detecting and Simulating Artifacts in GAN Fake Imagescited by 293
  5. 2020Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial Examplescited by 66
  6. 2022Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Inputcited by 49
  7. 2021Exploring Adversarial Fake Images on Face Manifoldcited by 24
  8. 2020Perception Matters: Exploring Imperceptible and Transferable Anti-forensics for GAN-generated Fake Face Imagery Detectioncited by 7
  9. 2020Adversarial Attacks on Co-Occurrence Features for GAN Detectioncited by 3
  10. 2021Making Generated Images Hard To Spot: A Transferable Attack On Synthetic Image Detectorscited by 3
  11. 2018Transformation on Computer-Generated Facial Image to Avoid Detection by Spoofing Detectorcited by 1

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