# A Realistic Hand Image Composition Method for Palmprint ROI Embedding Attack
## 5 Conclusions and Future Works  
This paper introduced an end-to-end, training-free method for hand image composition designed for ROI embedding attacks. This method leverages a modified style transfer approach, demonstrating notable success in achieving harmonized ROI images within a few iterations. The intuitive and effective utilization of a pretrained inpainting model to regenerate the area surrounding the embedding position contributes to the robustness of our framework. Notably, our approach requires only an ROI image and a hand image for execution, eliminating the need for training and enhancing practicality. In contrast with earlier approaches in palmprint attacks, our research introduces a novel full-hand attack strategy that builds upon an existing palmprint ROI. This innovative approach extends the applicability of attacks to more realistic environments, addressing a previously overlooked aspect. Furthermore, our method presents an intuitive and effective means of synthesizing a realistic composite hand image. Notably, this idea exhibits universality across different styles and achieves enhanced precision in texture preservation compared to conventional image composition methods. It is essential to highlight a key observation derived from our methodology: the palmprint recognition system is susceptible to significant threats even when exposed to minor and incomplete information leaks, such as an incomplete ROI. Moving forward, we will focus on devising a more efficient method that eliminates the need for Region of Interest (ROI) localization information from the target system. Additionally, our approach can be extended to a data augmentation strategy, synthesizing highly realistic full-hand images to enhance the performance of full-hand recognition methods. Nevertheless, we acknowledge that there is still ample room for improvement in our work. ROI harmonization is not so sensitive to the light color style and is susceptible to strong stains. In addition, the attack result should have a higher performance. However, we will aim to explore ways to enhance its efficacy in the future.  
Conceptualization, L.Y. and L.L.; methodology, L.Y.; software, L.Y.; validation, L.Y. and L.L.; formal analysis, L.Y.; investigation, L.Y.; resources, L.L.; data curation, L.L.; writing--original draft preparation, L.Y.; writing--review and editing, L.L., A.B.J.T. and C.K.; visualization, L.Y.; supervision, L.L. and A.B.J.T.; project administration, L.L.; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (61866028), the Technology Innovation Guidance Program Project (Special Project of Technology Cooperation, Science and Technology Department of Jiangxi Province) (20212 BDH81003), and the Innovation Foundation for Postgraduate Students of Nanchang Hangkong University (YC2022-S765).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy issues.  
Thank you to the reviewers who reviewed this paper and the MDPI editor who edited it professionally.  
The authors declare no conflicts of interest.