# Face De-Identification Using Convolutional Neural Network (CNN) Models for Visual-Copy Detection
## 6 Conclusions  
In this paper, we explored the viability of de-identification techniques in the context of image- and video-copy-detection tasks. Our proposed methods, leveraging feature inversion and D2 GAN, demonstrated consistent performance in preserving essential image features even after substantial modifications in the facial regions. This balance between protecting privacy and maintaining content integrity is pivotal in today's digital era, where data security and personal privacy are paramount. Our approach provides a novel pathway for media forensics, digital-rights management, and privacy-aware computing, marking a substantial advancement in the field. Looking ahead, further research could be directed towards enhancing the robustness and versatility of these de-identification methods. This includes refining the facial-detection process to reduce manual intervention and exploring the integration of more sophisticated neural-network architectures. Furthermore, the application of these methods could be broadened to encompass a wider range of media-analysis tasks, offering a comprehensive solution for privacy protection in various digital domains. Moreover, the application of these methods could be broadened to encompass a wider range of media-analysis tasks, offering a comprehensive solution for privacy protection in various digital domains. The implications of our research extend beyond technical innovation. They also contribute to the discourse on ethical AI, emphasizing the critical need for technologies that respect and protect user privacy. By pushing the boundaries of what is possible in media analysis and privacy preservation, we pave the way for future breakthroughs in creating more secure and privacy-conscious digital environments. Conceptualization, J.S. ; methodology, J.S. and J.K.; software, J.S. and J.K.; validation, J.S. and J.K.; formal analysis, J.S. and J.K.; investigation, J.S. and J.K.; resources, J.S. ; data curation, J.S. ; writing--original draft preparation, J.S. ; writing--review and editing, J.S. and J.K.; visualization, J.S. and J.K.; supervision, J.N. ; project administration, J.N. ; funding acquisition, J.N. All authors have read and agreed to the published version of the manuscript. This work was supported by an Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (RS-2023-00224740, development of technology to prevent and track the distribution of illegally filmed content). The datasets used for training and evaluating our models including the VGGFace2-HQ, FFHQ, CelebA-HQ, MS-Celeb-1M, Widerface, DISC21, and VCDB are publicly accessible. The VGGFace2-HQ dataset is available at [[https://github.com/NNNNAI/VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ)]([https://github.com/NNNNAI/VGGFace2-HQ](https://github.com/NNNNAI/VGGFace2-HQ)) (accessed on 4 January 2024); the FFHQ dataset can be found at [[https://github.com/NVlabs/ffhq-dataset](https://github.com/NVlabs/ffhq-dataset)]([https://github.com/NVlabs/ffhq-dataset](https://github.com/NVlabs/ffhq-dataset)) (accessed on 4 January 2024); the CelebA-HQ dataset is accessible at [[https://github.com/tkarras/progressive_growing_of_gans](https://github.com/tkarras/progressive_growing_of_gans)]([https://github.com/tkarras/progressive_growing_of_gans](https://github.com/tkarras/progressive_growing_of_gans)) (accessed on 4 January 2024); and the MS-Celeb-1M dataset can be accessed at [[https://exposing.ai/msceleb/](https://exposing.ai/msceleb/)]([https://exposing.ai/msceleb/](https://exposing.ai/msceleb/)) (accessed on 4 January 2024). The Widerface dataset is available at [[http://shuoyang1213.me/WIDERFACE/](http://shuoyang1213.me/WIDERFACE/)]([http://shuoyang1213.me/WIDERFACE/](http://shuoyang1213.me/WIDERFACE/)) (accessed on 4 January 2024).