我們需要一個演算法，對於一張可能被攻擊的影像，演算法能提供很高的召回率（TRP），低誤報率(FPR)。不僅如此，他還能夠對破壞的地方進行恢復。為了讓演算法能夠有高召回率，低誤報率，我們使用兩個龜殼矩陣來構建一個空間域，用來隱藏資料。我們用CRC來作爲我們的隱藏的數據的驗證。我們使用AMBTC來重構被破壞的區塊。我們通過演算法生成出來的影像PSNR達到了42.45db。不僅如此，在我們的實驗過程中，我們還發現了在現存的很多偵測演算法常用的refinement有可能會導致大量誤報的問題。因此，在我們方法的設計過程中，我們有效地利用CRC的驗證機制使我們提出的TS-IAR，可以不需要使用refinement，就額可以維持一個高的召回率（TPR），同時，不造成過去refinement所導致的誤報情況。

It is a challenge to design an image authentication scheme which has high TPR (true positive rate) but offers the lowest FPR (false positive rate). In this thesis, to increase the tamper detection ratio but maintain the lowest FPR, CRC is applied to our TS-IAR (turtle shell-based image authentication and recovery) scheme. Moreover, with the assistance of embedded BTC coefficients and the turtle shell-based hiding strategy, the PSNRs of the watermarked images is significantly improved and up to 42.45 dB on average compared with existing schemes, and the recovery feature can be guaranteed. During the experiments, we also found that the refinement procedure can increase that the TPR but it also could cause unexpected false alarm. By adopting the CRC coworking with our proposed TS-IAR scheme, the tamper detection performance can be guaranteed without causing false alarm which frequently being caused by the refinement procedure.

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