由於影像編輯軟體快速的發展,使得數位影像可藉由影像編輯軟體修改其影像內容資訊,成為一張全新的影像。外來拼接篡改為一種簡單又常見的篡改方式。此種竄改操作通常是複製影像的一個區域,貼至另一張影像當中,試圖將原本不屬於該影像的內容加入到影像當中,因此數位影像的真實性備受考驗。 一張影像的生成,通常會經由不同的感測器與後處理而產生,所以不同來源的影像會含有不同大小的雜訊量,基於此現象,本論文利用多尺度主成分分析估測雜訊程度演算法來偵測影像拼接,以改善現有文獻偵測率之問題。此外,我們利用EGB 影像分割(segmentation)的結果,與先做雜訊程度估測再經分群後的結果做結合,用以獲得最佳的篡改區域定位圖。 在實驗階段,我們用哥倫比亞圖像拼接檢驗資料庫(Columbia uncompressed image splicing detection evaluation dataset)當作我們的測試資料庫,從實驗結果顯示,我們提出利用多尺度區域雜訊不一致性之影像拼接偵測較現有文獻方法更加強健與準確。
Due to the rapid development of image editing software, it becomes easy to tamper a digital image and create a fake image. One of the common and simple ways of the digital image tampering is image splicing, which is to copy a region of an image to another image, and add the contents not belonging to the original image. Therefore, the authenticity of digital image has become an important issue. Since different sources of images are productions of different sensors and post-processing, they will contain different noise levels. Based on this fact, we propose a multi-scale PCA noise level estimation algorithm to detect image splicing, and improve the detection rate of the existing works. In addition, our method can locate the tampering region by clustering the estimated noise with the integration of EGB segmentation. In the experiments, we use the Columbia uncompressed image splicing detection evaluation dataset to test our method. The experimental results show that our method can detect the image splicing robustly and accurately.
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