# Enhancing CT Segmentation Security against Adversarial Attack: Most Activated Filter Approach
## 4 Conclusions  
This research explored the vulnerabilities introduced by adversarial attacks in deep-learning-driven medical imaging. Utilizing real-world CT and gastrointestinal polyp datasets, the potency of early-layer features for identifying these threats was highlighted. Through exhaustive experimental results, the proposed method's superior performance over baseline approaches is demonstrated, further substantiated by comparative evaluations with various classifiers. The proposed technique stands out in its robustness against these adversarial attempts. Additionally, visual aids equip physicians with the clarity needed to differentiate authentic scans from tampered ones. As the landscape of adversarial strategies and deep learning architectures evolves, it is crucial for the subsequent research to remain on the forefront, continuously updating our defensive strategies. We strive to reinforce the trust in deep learning's role in medical imaging, affirming its consistent dependability in healthcare settings.  
In the future work, we aim to test our approach using a variety of medical imaging techniques, including MRI and X-ray, to provide a wide-ranging safeguard in the field of medical imaging. Furthermore, we intend to mitigate the adverse activation impacts caused by adversarial samples, focusing on those filters that our framework identifies as being highly activated.  
**Author Contributions:** Conceptualization, W.L. and Y.K.; methodology, W.L.; software, W.L.; validation, W.L.; formal analysis, W.L.; investigation, W.L.; data curation, W.L.; writing--original draft preparation, W.L.; writing--review and editing, W.L. and Y.K.; visualization, W.L.; supervision, Y.K.; project administration, Y.K.; funding acquisition, Y.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Seoul R&BD Program (No. RO230041). Additionally, this work was supported by the Institute of Information and communications Technology Planning and Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-2022-00155885, Artificial Intelligence Convergence Innovation Human Resources Development (Hanyang University ERICA)). It was also supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF).  
**Institutional Review Board Statement:** Not applicable.  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** Restrictions apply to the availability of these data. The data were obtained from \"Multi-Atlas Labeling Beyond the Cranial Vault--Workshop and Challenge\", available at [[https://www.synapse.org/#Synapse:syn3193805/wiki/217789](https://www.synapse.org/#Synapse:syn3193805/wiki/217789)]([https://www.synapse.org/#Synapse:syn3193805/wiki/217789](https://www.synapse.org/#Synapse:syn3193805/wiki/217789)), accessed on 9 August 2022, and the \"Kvasir SEG--Segmented Polyp Dataset for Computer Aided Gastrointestinal Disease Detection\" accessible at [[https://datasets.simula.no/kvasir-seg/](https://datasets.simula.no/kvasir-seg/)]([https://datasets.simula.no/kvasir-seg/](https://datasets.simula.no/kvasir-seg/)), accessed on 1 February 2024.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.