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  Department of Robotics and Mechatronics Engineering Theses Ph.D.

Deep Learning-Based Privacy-Preserving Optical Image Captioning

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dc.contributor.advisor 문인규 -
dc.contributor.author Martin Antoinette Deborah -
dc.date.accessioned 2026-01-23T10:53:58Z -
dc.date.available 2026-01-23T10:53:58Z -
dc.date.issued 2026 -
dc.identifier.uri https://scholar.dgist.ac.kr/handle/20.500.11750/59605 -
dc.identifier.uri http://dgist.dcollection.net/common/orgView/200000942999 -
dc.description Deep Learning, Privacy-Preserving, Image Captioning, Partial Encryption, Photon Counting Imaging (PCI), Poisson–Multinomial Distribution–based PCI. -
dc.description.tableofcontents ABSTRACT i
List of Contents ii
List of Tables iv
List of Figures vi
1. INTRODUCTION 1
1.1. Motivations and Objectives 1
1.2. Overview 5
1.3. Contributions and Outline 6
2. PRIVACY-PRESERVING IMAGE CAPTIONING USING DOUBLE RANDOM PHASE ENCODING (DRPE) 8
2.1. Double Random Phase Encoding (DRPE) 8
2.2. Full DRPE-Based Image Captioning 10
2.2.1 Methodology 10
A. Overview 10
B. Encoder 11
C. Decoder 12
2.2.2. Experiments 15
A. Dataset 15
B. Implementation Details 17
C. Evaluation Metric 17
D. Results 20
2.3. Partial DRPE-Based Image Captioning 26
2.3.1. Methodology 26
A. Partial Encryption Using DRPE 26
B. Overview of Proposed Method 26
C. Dual-Stream Encoder 27
D. Transformer 29
2.3.2 Experiments 30
A. Dataset 30
B. Implementation Details 32
C. Ablation Study 32
1) Effect of Dual-Stream Encoder 32
2) Transformer Architecture Analysis 33
3) GloVe Embeddings 34
4) Fine-tuning Strategies on the Encoder 35
D. Quantitative Results and Visualization 37
E. Comparison with State of the Art 48
F. Additional Experiments 50
3. PRIVACY-PRESERVING OPTICAL IMAGE CAPTIONING USING PHOTON-LIMITED AND FEDERATED LEARNING 56
3.1. Methodology 56
3.1.1. Poisson-Multinomial Distribution-based Photon Counting Imaging (PMD-PCI) 56
3.1.2. Model Architecture 57
3.1.3 Federated Learning Framework 60
3.2. Experiments 64
3.2.1. Dataset 64
3.2.2. Implementation Details 64
3.2.3. Results 65
A. Centralized Training 67
B. Federated Learning 81
Ⅳ. CONCLUSION AND FUTURE WORK 96
4.1. Summary and Discussion 96
References 100
요 약 문 108		 -
dc.format.extent 108 -
dc.language eng -
dc.publisher DGIST -
dc.title Deep Learning-Based Privacy-Preserving Optical Image Captioning -
dc.title.alternative 딥러닝 기반 프라이버시 보호 광학 이미지 캡셔닝 -
dc.type Thesis -
dc.identifier.doi 10.22677/THESIS.200000942999 -
dc.description.degree Doctor -
dc.contributor.department Department of Robotics and Mechatronics Engineering -
dc.date.awarded 2026-02-01 -
dc.publisher.location Daegu -
dc.description.database dCollection -
dc.citation XT.RD M383 202602 -
dc.date.accepted 2026-01-19 -
dc.contributor.alternativeDepartment 로봇및기계전자공학과 -
dc.subject.keyword Deep Learning, Privacy-Preserving, Image Captioning, Partial Encryption, Photon Counting Imaging (PCI), Poisson–Multinomial Distribution–based PCI. -
dc.contributor.affiliatedAuthor Martin Antoinette Deborah -
dc.contributor.affiliatedAuthor Inkyu Moon -
dc.contributor.alternativeName 마틴 앙투아네트 데보라 -
dc.contributor.alternativeName Inkyu Moon -
dc.rights.embargoReleaseDate 2031-02-28 -
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