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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳銘憲(Ming-Syan Chen) | |
| dc.contributor.author | Wei-Pin Wang | en |
| dc.contributor.author | 王威斌 | zh_TW |
| dc.date.accessioned | 2021-07-11T14:54:10Z | - |
| dc.date.available | 2025-09-01 | |
| dc.date.copyright | 2020-08-20 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-17 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/78383 | - |
| dc.description.abstract | 圖片隱寫術是一種將機密資訊轉為微小波動再嵌入圖片的技術，傳統作法只能將極為少量的資訊藏於一張圖片上，近期使用深度學習的方式能將隱藏量提升10到100倍，使彩色圖片中的一個像素能藏入24位元的資料，但這種方式對於圖片的變動非常敏感，特別是JPEG壓縮的損壞會使藏入的資訊無法還原。而在影片隱寫術方面，一般是把影片中每一幀當作獨立的圖來套用圖片隱寫術，但這並沒有善用到每幀的時序特性，在本研究中，我們提出了兩種深度學習模型分別用於圖片和影片隱寫術，我們設計了端對端抗JPEG具注意力機制的模型(JASN)，它可以把一張機密圖像藏入另一張同等大小的圖片，即使該圖被JPEG壓縮過，我們仍然可以還原機密圖片。至於影片部分，我們設計一種高效的模型(FVSN)，在執行速度、模型大小和影片品質上，它的表現都比現存最好的模型還要好。 | zh_TW |
| dc.description.abstract | Image steganography is a technique to hide secret information into an ordinary image by adding visually indistinguishable perturbations. Traditional methods can only embed a small amount of data into the carrier image. The latest deep learning-based methods have achieved 24 bits payloads per pixel which is 10 to 100 times larger than typical methods. However, existing methods are sensitive to image distortion, especially the JPEG lossy compression, which makes models fail to reconstruct the secret. In video steganography, people usually extract the frames from videos and consider them as the image steganography task, but underutilize the temporal features. In this study, we proposed two deep networks for image and video steganography individually. We designed an end-to-end JPEG-resistant attention model, called JASN, which can robustly embed a secret image to a same-size cover image, and successfully reveal the secret image after processed by JPEG algorithms. We also designed an efficient video model, called FVSN, which outperforms the state-of-the-art model on speed, model size, and video quality. | en |
| dc.description.provenance | Made available in DSpace on 2021-07-11T14:54:10Z (GMT). No. of bitstreams: 1 U0001-1007202021582200.pdf: 14950521 bytes, checksum: 9810f70ea0542f89d7c4552d0b79ab4a (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 口試委員審定書 i 誌謝 ii 摘要 iii Abstract iv Contents vi ListofFigures ix ListofTables x 1 Introduction 1 2 RelatedWork 5 2.1 U-net 5 2.2 Steganography 6 2.3 Robust SteganographyandWatermarking 7 2.4 VideoSteganography 7 3 JASNFramework 8 3.1 AttentionHidingNetwork 8 3.1.1 FeatureExtractor 9 3.1.2 JPEG-guidedAttentionMachine 9 3.1.3 SecretEncoder 10 3.1.4 Secret∗Decoder 11 3.2 JPEGSimulator 11 3.3 RevealingModel 12 3.4 LossFunction 13 3.5 ScoreFunctions 15 4 Experiments onImageSteganography 17 4.1 Dataset andImplementationDetails 17 4.2 ResultsandPerformance 19 4.3 AblationStudy 20 4.4 HidingPlace 22 4.5 DifferentJPEGCompression 23 4.6 Multiple TimesJPEGCompression 25 4.7 Hyperparameters 27 5 FVSNFramework 28 5.1 FVSNStructures 28 5.2 LossFunction 30 5.3 ScoreFunctions 31 6 Experiments onVideoSteganography 32 6.1 Datasets andImplementationDetails 32 6.2 ResultsandPerformance 33 6.3 Non-ContinuousFrames 36 6.4 ModelsAnalysis 37 6.4.1 ModelSize 37 6.4.2 ExecutionTime 39 6.5 Steganalysis 40 6.5.1 UnsupervisedSteganalysis 40 6.5.2 SupervisedSteganalysis 41 6.6 Hyperparameters 43 7 Conclusion 44 References 45 | |
| dc.language.iso | en | |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 影片隱寫術 | zh_TW |
| dc.subject | 穩健資訊隱藏 | zh_TW |
| dc.subject | JPEG強健性 | zh_TW |
| dc.subject | 高容量 | zh_TW |
| dc.subject | 影像隱寫術 | zh_TW |
| dc.subject | 高效模型 | zh_TW |
| dc.subject | 注意力機制 | zh_TW |
| dc.subject | Attention mechanism | en |
| dc.subject | Efficient model | en |
| dc.subject | Image steganography | en |
| dc.subject | High capacity | en |
| dc.subject | JPEG robustness | en |
| dc.subject | Robust information hiding | en |
| dc.subject | Video steganography | en |
| dc.subject | Deep learning | en |
| dc.title | 高容量與穩健資訊隱藏技術:圖片及影片隱寫術 | zh_TW |
| dc.title | High Capacity and Robust Information HidingTechnology: Steganography on Image and Video | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 王鈺強(Yu-Chiang Wang),陳怡伶(Yi-Ling Chen),楊得年(De-Nian Yang),賴冠廷(Kuan-Ting Lai) | |
| dc.subject.keyword | 注意力機制,深度學習,高效模型,影像隱寫術,高容量,JPEG強健性,穩健資訊隱藏,影片隱寫術, | zh_TW |
| dc.subject.keyword | Attention mechanism,Deep learning,Efficient model,Image steganography,High capacity,JPEG robustness,Robust information hiding,Video steganography, | en |
| dc.relation.page | 53 | |
| dc.identifier.doi | 10.6342/NTU202001435 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-08-18 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2025-09-01 | - |
| 顯示於系所單位： | 電機工程學系 | |
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