Image tampering detection based on RDS-YOLOv5 feature enhancement transformation.

ABSTRACT: Malicious image tampering has gradually become another way to threaten social stability and personal safety. Timely detection and precise positioning can help reduce the occurrence of risks and improve the overall safety of society. Due to the limitations of highly targeted dataset training and low-level feature extraction efficiency, the generalization and actual performance of the recent tampered detection technology have not yet reached expectations. In this study, we propose a tampered image detection method based on RDS-YOLOv5 feature enhancement transformation. Firstly, a multi-channel feature enhancement fusion algorithm is proposed to enhance the tampering traces in tampered images. Then, an improved deep learning model named RDS-YOLOv5 is proposed for the recognition of tampered i

SUBMITTER: Zhu M 

PROVIDER: S-EPMC11525581 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature