Robust deepfake detector against deep image watermarking

Jian Yu, Xin Liu, Fengbiao Zan and Yanhan Peng

PLOS ONE, 2025, vol. 20, issue 12, 1-13

Abstract: Deepfake technology poses a significant threat to information security,rendering deepfake detection research crucial. However, current detection methods experience a marked performance degradation in the presence of deep watermarking within images. In this paper, we propose a multi-module model, which integrates Efficient Multi-scale Attention within Xception as the detection module and introduces a feature dropout module to eliminate redundant image features. Experimental results demonstrate that when 50% and 100% of the images in the dataset contain MBRS watermarks, the accuracy (ACC) metrics of our model are comparable to those of existingbaseline models. However, when 50% and 100% of the images contain FaceSigns watermarks, the ACC metrics of our model outperform those of other baseline models by approximately 10% and 20%, respectively.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0338778 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 38778&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0338778

DOI: 10.1371/journal.pone.0338778

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().