基於雙路徑網路之影像隱寫分析;Steganalysis in Digital Images based on Dual Path Networks

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題名:	基於雙路徑網路之影像隱寫分析;Steganalysis in Digital Images based on Dual Path Networks
作者:	高珮涵;Kao, Pei-Han
貢獻者:	資訊工程學系
關鍵詞:	隱寫分析;隱寫術;深度學習;卷積神經網路;Steganalysis;steganography;deep learning;convolutional neural networks
日期:	2020-08-13
上傳時間:	2020-09-02 18:05:39 (UTC+8)
出版者:	國立中央大學
摘要:	隱寫術(Steganography)是將若干影像甚至音視訊等資料做為載體，再把大量機密資訊嵌入於其中以達成秘密通訊的效果，而隱寫分析(Steganalysis)則是偵測可能的載體以確認秘密資訊是否藏於其中。關於隱寫分析，以往多採用人工設計之特徵擷取，但需要耗費較多人力以及依賴相關研究經驗，近期則以深度學習技術為主，但也多使用指定的濾波器對待測資料進行人工預處理，無法達成完全的自動學習。本研究主要為影像隱寫分析，使用雙路徑卷積神經網路(Dual Path Networks, DPN)達成端到端(end-to-end)架構，以ResNet 擷取特徵，再以 DenseNet 提取更深層且細微的特徵，結合兩者的優勢，組成權值共享的雙通道區塊(DPN blocks)，並採用 ResNeXt 的分組卷積降低計算量，使用不同參數的雙通道區塊組合以利隱寫分析。SRNet 為目前隱寫分析模型中效果較為優異者，當中採用了 ResNet 作為特徵擷取，我們將其替換為雙通道區塊進行比較，偵測準確度有所提升，也證實了 DPN 有助於隱寫特徵的擷取。接著我們將整體架構改為以 DPN 為主，與以往的隱寫分析架構不同，並與這些架構比較以彰顯所提出架構的可行性。關鍵字：隱寫分析、隱寫術、深度學習、卷積神經網路。;Steganography is a technique to embed a large amount of information in such carriers as images, audio, videos and even texts to achieve effective secret communications. On the other hand, steganalysis is the adversarial technique aimed at determining whether the investigated carriers contain hidden information. In the field of steganalysis, heuristic features were usually adopted. Recently deep learning techniques are often employed but most existing methods still use certain high-pass filters to apply pre-processing. In this research, we focus on image steganalysis and adopt the dual path networks (DPN) to achieve an end-to-end architecture. The proposed scheme uses ResNet to extract features, and then employs DenseNet to extract deeper and smaller features. It combines the advantages of both networks to form a DPN blocks with shared weights. The scheme uses the group convolution to reduce the amount of computation. Finally, dual path blocks with different parameters are tested to build suitable steganalysis architectures. SRNet, which uses ResNet, performs quite well in image steganalysis. We first replace its ResNet blocks with DPN blocks for comparison. The detection accuracy is improved and confirms that the structure using DPN is helpful to steganalysis. We then use DPN blocks to build our architecture and then compare the performance with the existing steganalysis architectures. Finally, we use the ALASKA II dataset to verify the feasibility of the proposed scheme. Index Terms - Steganalysis, steganography, deep learning, convolutional neural networks.
顯示於類別:	[資訊工程研究所] 博碩士論文

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