Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/72817
標題: 可預測之回復式資訊隱藏
Predictable+Reversible+Image+Data+Hiding
作者: 郭慧彬
Guo, Huei-Bin
王宗銘 
Wang, Chung-Ming
關鍵字: reversible data hiding;可回復式資訊隱藏;prediction mechanism;hybrid predictor;user demands;預測機制;混合預測器;使用者需求
出版社: 國立中興大學工學院;Airiti Press Inc.
Project: 興大工程學刊, Volume 23, Issue 3, Page(s) 91-118.
摘要: 
本論文提出一個可回復式資訊隱藏的預測機制。對於此預測機制的前置作業,首先,我們考慮秘密訊息"0"與"1"的分佈機率情形,並且評估掩護影像的像素值來建立對應的五個直方圖。接著,我們根據此預測機制所提出的預測運算來計算出嵌入效率、嵌入量以及影像品質。最後,便可依據使用者的需求來提供相對應的數據結果。此預測機制也可以擴展到對影像資料庫上的預測,這使得我們可以更快速的從影像資料庫中找到最適合使用者所需求的影像。其中,我們捨棄以單一像素預估器來預估像素值,於本篇論文首次提出混合六種像素預估器來提高預估像素值的準確度,並且進一步的來增加訊息的嵌入藏量。實驗結果顯示,我們可以藉由此預測機制,即可評估出掩護影像嵌入訊息後的各種數據。而且,我們根據使用者依嵌入量或影像品質所提出的條件,也能預測出滿足使用者條件之結果。並且,以四個影像資料庫而言,我們使用此預測機制更能迅速的找到最佳嵌入效率之影像。其中,我們混合六種預估器對掩護影像的嵌入量做出評估,所評估出的準確率可以高達99.9%。本文所提出的預測機制有四項優點:能以準確的預測技術來得知嵌入後的結果;以使用者角度而言,能更便利的得知各種不同條件上的嵌入結果;我們可以將此預測機制拓展至影像資料庫上,進而可以更快速的回饋使用者;我們利用混合六種預估器能更準確的評估出像素值的結果,可進一步增加嵌入藏量。我們認為所提出的可預測機制進一步的擴大可回復式資訊隱藏的應用範疇。

In this paper, we provide a prediction mechanism for reversible data hiding. Given a cover image, we construct and analyze five corresponding histograms. We further operate the prediction computing taking into account the probability distribution of the secret bit "0" and "1." Consequently, our scheme can report the embedding efficiency, embedding capacity, and image quality prior to the real message embedding. We extend our scheme to an image database. This allows us to suggest the most appropriate image achieving the highest efficiency within an image database. During the pixel prediction, a variety of six predictors is employed. This greatly improves the prediction accuracy and increases the embedding capacity that can conceal in a cover image. The experiments show that the proposed prediction scheme can foresee the embedding information without practically embedding secret messages into a cover image. The scheme can recommend a proper image satisfying the user's demands for the image quality and the embedding capacity. The approach of using the hybrid predictor leads to a faithful prediction where the prediction accuracy in average can be as high as 99.9% within four test image databases. In conclusion, our prediction scheme offers four contributions: it can accurately predict the results prior to message embedding; the scheme can response to user's demanding for the image quality and the embedding capacity; the most suitable image within an image database can be recommended which can achieve the best embedding performance; a hybrid approach we introduce enables us to improve the prediction accuracy and increase the embedding capacity.
URI: http://hdl.handle.net/11455/72817
ISSN: 1017-4397
Appears in Collections:第23卷 第3期
工學院

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