博碩士論文 106522009 詳細資訊




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姓名 胡家銘(Jia-Ming Hu)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 基於密集連接卷積神經網路之JPEG影像隱寫分析
(Steganalysis in JPEG Images based on Densely Connected Convolutional Neural Network)
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檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2022-9-1以後開放)
摘要(中) 隱寫術(Steganography)是將大量資料隱藏於如影像、視訊等載體之技術,而隱寫分析(Steganalysis)為隱寫術的對抗技術,用於判斷載體中是否隱藏額外資訊。本研究藉由深度學習(Deep Learning),訓練一個由密集連結的卷積神經網路(Convolutional Neural Networks,CNN)模塊為主體,設計一個針對JPEG影像隱寫術的全新隱寫分析架構。本架構結合Inception Net、ResNet與DenseNet的設計巧思與優勢,包括Inception Net中結合多種不同尺度卷積核以增加對尺度的適應性,ResNet對於特徵擷取的高重複使用率且冗餘程度低,而DenseNet可創造新特徵但冗餘程度高等特性,設計出效果更好的隱寫分析架構。本研究的模型訓練不需要人工預處理,可自動學習有效特徵。在特徵提取的部分,本機制不使用池化操作以避免抑制隱寫訊號,偵測則以三種隱寫術為對象。此外,我們設計所謂「多模型決策方法」,結合多個單一模型一起檢測,並以原先的個別單一訓練模型做為比較對象。實驗結果顯示我們的模型和方法,幾乎都超越了比較的目標,甚至是目前最先進的隱寫分析模型-SRNet。
摘要(英) Steganography is a technique for hiding large amounts of data in carriers such as video and video, and Steganalysis is a technique to determine whether additional information is hidden in the carrier. In our study, Deep Learning is used to train a densely connected Convolutional Neural Networks (CNN) module to design a new steganalysis architecture for JPEG image steganography. This architecture combines the design ingenuity and advantages of Inception Net, ResNet, and DenseNet.Including the inclusion of multiple scale convolution kernels in Inception Net to increase scalability, and ResNet′s high reuse and low redundancy for feature extraction. DenseNet can create new features but high levels of redundancy to design a better steganalysis architecture. The model training of our study does not require manual preprocessing and can automatically learn effective features. In the feature extraction part, the module does not use the pooling operation to avoid suppressing the steganographic signal, and the detection is based on three steganography. In addition, we design a so-called "multi-model decision method" that combines multiple single models to detect together and compares with the original individual training models. The experimental results show that our models and methods almost surpass the goal of comparison, even the most advanced steganalysis model - SRNet.
關鍵字(中) ★ 隱寫分析
★ 隱寫術
★ 深度學習
★ 卷積神經網路
關鍵字(英) ★ Steganalysis
★ Steganography
★ Deep Learning
★ Convolutional Neural Networks.
論文目次 論文摘要 I
Abstract II
目錄 III
附圖目錄 V
表格目錄 VI
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究貢獻 2
1.3 論文架構 4
第二章 隱寫分析相關研究 5
2.1 隱寫術 5
2.1.1非自適應隱寫術 5
2.1.2自適應隱寫術 6
2.1.3各個影像嵌入域中常見的隱寫術 6
2.2 基於人工特徵的隱寫分析 7
2.3 卷積神經網路 10
2.4 基於深度學習的隱寫分析 12
第三章 提出方法 15
3.1 卷積神經網路的演進 15
3.1.1 Inception系列網路 15
3.1.2深度殘差網路:ResNet 17
3.1.3密集連接卷積神經網路:DenseNet 18
3.2網路模型建立與其訓練流程 20
3.3驗證網路模型設計的小實驗 35
第四章 實驗結果 38
4.1 開發環境與資料集介紹 38
4.2 偵測結果 42
4.3 多模型決策方法 46
第五章 結論與未來展望 51
參考文獻 52
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指導教授 蘇柏齊(Po-Chyi Su) 審核日期 2019-8-20
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