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https://hdl.handle.net/10356/182488
Title: | Unlearnable example with face images | Authors: | Peng, Haohang | Keywords: | Computer and Information Science | Issue Date: | 2024 | Publisher: | Nanyang Technological University | Source: | Peng, H. (2024). Unlearnable example with face images. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/182488 | Abstract: | This thesis focuses on the critical issue of image protection, with a particu- lar emphasis on safeguarding face images by introducing perturbations to these images. Deep learning models have demonstrated remarkable potential to drive significant advancements across various fields, including the generation of new images and the enhancement of object detection capabilities. However, alongside these advancements, there are inherent risks to personal privacy that cannot be overlooked. These risks arise through multiple avenues, especially in light of the rapid development of generative AI technologies such as stable diffusion, which empower individuals to create images using just a few reference pictures. To address this pressing problem, this research delves into the intricate rela- tionship between unlearnable examples (UEs) and deep learning models. We conduct a thorough analysis of how UEs can be effectively applied within the realm of generative AI. Furthermore, we extend our investigation to the use of UEs in object detection, aiming to ensure that models are unable to accurately detect or interpret these images, thereby enhancing privacy protection measures in the process. | URI: | https://hdl.handle.net/10356/182488 | Schools: | School of Electrical and Electronic Engineering | Fulltext Permission: | restricted | Fulltext Availability: | With Fulltext |
Appears in Collections: | EEE Theses |
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File | Description | Size | Format | |
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Peng Haohang-Dissertation-revised.pdf Restricted Access | 5.58 MB | Adobe PDF | View/Open |
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