5. Abstract
Digital contents have become increasingly popular nowadays due to
their convenience of transferring and storage. In addition, the rapid
growth of broadband networks and advanced coding technologies make
creation and distribution of digital contents much easier and faster than
ever. However, digital contents can be easily modified and the malicious
tampering of data may change the meaning of contents. In this research,
we propose a digital watermarking scheme under the framework of
H.264/AVC. The watermark is embedded into video frames to ensure the
correct frame order. Such attacks as frame dropping, swapping or
insertion can be reflected from the unambiguous watermark detection.
We use DC values of blocks as the features of the image, which can
help determine whether a shot change occurs. The image hash value is
calculated by content analysis and used to generate the watermark
sequence. The watermarking scheme uses a human perceptual model to
adjust the watermark energy so that the watermark robustness can be
enhanced without degrading the visual quality.
By integrating the watermarking approach with H.264/AVC, we can
make the watermark embedding/detection be done in a very efficient
manner. The content-analysis mechanism not only makes the watermark
imperceptible but simplifies the watermark detection process. The
experimental results show that the embedded watermark can survive
transcoding processes such as changing quality parameters or the coding
structure. Besides, the frame modification attacks will be revealed by the
successful watermark extraction.
-i-
33. P畫面在編碼時利用參考畫面找尋內容最為相似的區塊 因此利
,
用P畫面編碼時估計的移動向量,我們可以直接以參考畫面中的DC圖
做預測,如圖10,由此方法即可在頻率域下計算求得DC圖,而不需
要將畫面預測的DC值即為對應的區塊面積比例乘以DC值。
A B
N1 N2
N3 N4
P
C D
Reference frame Current frame
圖10 P畫面中區塊DC值計算方式
N1 N N N
DCP = ( ) DCblockA + ( 2 ) DCblockB + ( 3 ) DCblockC + ( 4 ) DCblockD (3.5)
N N N N
DC圖為影片亮度的特徵縮圖,如圖11,由DC圖得到的區塊亮度
值可以用來計算畫面中的亮度分布,作為判斷前後張畫面之間的關聯
性、畫面的特徵等資訊。
- 21 -
41. 最後我們對純量量化完的圖再做一次離散餘弦轉換取DC值 該值
,
即為我們計算得到的影像雜湊值,也就是我們所定義該畫面的特徵
值。
浮水印序列生成:
首先我們對於所要編碼的畫面,將該畫面號碼做為浮水印序列生
成的參數,選取其號碼所對應之Hadamard序列(Hadamard sequence)。
我們已知Hadamard序列彼此互為正交關係,利用此序列作為所要嵌
入之浮水印序列,則浮水印之間彼此關聯性最低,對於浮水印的偵測
最為有利。接著利用畫面內容分析得到的影像雜湊值,與前一鏡頭片
段畫面內容所產生的擾亂序列,對浮水印序列相乘,以改變其值,最
後所生成之序列即為欲嵌入之浮水印序列。
3-2-2 視訊浮水印嵌入步驟
對浮水印的嵌入過程有了充分的了解後,我們將整個嵌入的步驟
做完整的條列式說明:
Step 1:浮水印的前置處理,計算畫面之 DC 圖,用以做為畫面內容
分析之依據。
Step 2:利用與前一張畫面彼此的 DC 圖做影像內容分析,判斷是否
有做畫面鏡頭切換,計算同一個鏡頭場景中之畫面內容。
Step 3:對目前畫面的 DC 圖做模糊化等處理,並且再對其取 DC 值,
做影像雜湊運算得到一雜湊值。
Step 4:利用 Step2 和 Step3 得到的值產生浮水印之擾亂序列。
Step 5:取第 n 組 Hadamrad 序列代表影片中的的第 n 個 I 畫面,若是
第 n 個 I 畫面以超過浮水印的數目 則回去取第 1 組 Hadamard
,
序列。
- 29 -
42. Step 6:利用上述之擾亂序列對 Hadamard 序列做擾亂重排之處理。
Wi = H i * Ri
Step 7:計算欲嵌入的區塊位置之 Watson’s model,以 Watson’s model
調整嵌入的浮水印能量大小,以符合人眼對影像的敏感度。
Wi ' = w * Wi
Step 8:將產生之浮水印嵌入影片中。
Cwi = Coi + Wi '
3-3 視訊浮水印偵測與驗證流程
如同浮水印的嵌入過程,偵測視訊浮水印結合於視訊解碼過程
中,圖 18 為加入浮水印偵測之視訊解碼流程圖:
圖 18 浮水印偵測之視訊解碼流程圖
3-3-1 畫面DC圖重建
在浮水印的偵測過程中,必須先由視訊解碼取得畫面特徵、鏡頭
場景等資訊,才能重建浮水印系統中的擾亂序列,因此在解碼過程
中,我們必須要先重建影像畫面的 DC 圖。DC 圖的重建過程與編碼
時 DC 圖的生成方式類似,I 畫面由解碼後的區塊重建 DC 值;P 畫
面的移動向量由解碼過程中可以直接取得,並依移動向量取參考畫面
- 30 -
43. 的 DC 值預測產生。
3-3-2 浮水印序列重建
擾亂序列的重建方式與擾亂序列在編碼過程中的生成方式相
同,利用重建之畫面DC圖,我們可以對其做影像內容分析判斷場景
之切換以及影像雜湊計算;利用前一鏡頭場景之畫面內容與影像雜湊
值,我們便可以推導出浮水印系統的擾亂序列,流程如圖19。
⊗
圖19 浮水印序列重建流程圖
3-3-3 視訊浮水印偵測
將所有浮水印序列以推導出的擾亂序列做順序上的重排,並以此
序列乘上解碼後之畫面各區塊數值計算各組浮水印的關聯性;比較各
組浮水印與畫面中浮水印之關聯性,關聯性最高的該組浮水印即為此
畫面中所嵌入之浮水印號碼;因此我們可以由浮水印號碼得知此 I 畫
面在此場景中之順序。
3-3-4 視訊浮水印偵測步驟
在解碼過程中,對於視訊浮水印的完整偵測步驟為:
Step 1:浮水印的前置處理,計算畫面之 DC 圖,用以做為畫面內容
分析之依據。
Step 2:利用與前一張畫面彼此的 DC 圖做影像內容分析,判斷是否
有做鏡頭場景切換,以得到之鏡頭場景區段計算區段內之畫
- 31 -
44. 面內容。
Step 3:對目前畫面的 DC 圖做模糊化、純量量化處理,並且再對其
取 DC 值,做影像雜湊計算得到一雜湊值。
Step 4:對於目前欲偵測浮水印之 I 畫面所在之前一場景鏡頭,以 Step
2 計算得到的該區段內之畫面內容,和 Step3 得到的雜湊值產
生浮水印的擾亂序列。
Step 5:對所有 Hadamard 序列做擾亂處理,得到浮水印序列。
Wi = H i * Ri
Step 6:對所有浮水印序列乘上嵌入浮水印的影片。
Step7:比較各組浮水印與畫面中浮水印之關聯性,若關聯性最高的
該組超過一預設之門檻值,則此組浮水印即為該畫面中所嵌
入之浮水印。
3-4 利用浮水印對視訊畫面之驗證
浮水印偵測時,在解碼過程中計算可得前一場景區段內之畫面
內容以及 I 畫面之影像雜湊值,利用此二值產生的擾亂序列與
Hadamard 序列生成的浮水印序列偵測而得之浮水印值即為此 I 畫面
於場景中之順序(Frame number),如圖 20。
圖20 影片中I畫面與所嵌入之浮水印關係
本論文所提出之方法可作為影片內容之驗證,因此我們列出影
- 32 -
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