由於數位相機與智慧型手機的普及，人們可以輕易地取得各式高解析度數位影像，而便利的相片編修工具讓幾乎所有的使用者都能自行修改數位影像，這也意味著數位影像內容有可能受到有心人士的竄改，並將其網路或社群網站中散布，更改的影像不僅混淆視聽，更可能被作為操縱輿論的工具。然而，目前對於多樣化的影像竄改方式仍無完善應對的方法，數位影像內容的真實性因此受到若干質疑。

在影像鑑識領域中，一個重要的分支為來源相機模型的辨識，本研究以相機模型辨識為基礎，提出可運用於偵測各種影像畫面竄改的影像鑑識架構。所提出的方法不需要使用竄改影像作為訓練資料，而是採用原始影像或相片自身資訊，透過卷積神經網路，設計能夠學習相機模型的通用特徵提取器，接著運用孿生網路來學習比較兩個圖片區塊是否具備一致性，再根據比較結果選取適當的竄改區域，接著透過前景提取技術精修竄改區域。本研究的主要貢獻為 (1) 將影像區域一致性的研究延伸至影像鑑識應用、(2) 設計更好的區塊比較模式、(3) 改善竄改區域準確度。實驗結果證實本機制的實用性，並與現有方法的評比中取得最好的效果。

Identifying the type of a camera used to capture an investigated image is a useful image forensic tool, which usually employs machine learning or deep learning techniques to train various models for effective forgery detection. In this research, we propose a forensic scheme to detect and even locate image manipulations based on deep-learning-based camera model identification. Since the ways of tampering images are very diverse, it’s difficult to collect enough tampered images for supervised learning. The proposed method avoids using tampered images of various kinds as the training data but employ the information of original pictures. We first train a convolutional neural network to acquire generic features for identifying camera models. Next, the similarity measurement using the Siamese network to evaluate the consistency of image block pairs is employed to locate approximate tampered areas. Finally, we refine the tampering areas through a segmentation network.

The main contributions of this research include: (1) extending the study of image region consistency to image forensics applications, (2) designing a better block comparison algorithm, and (3) improving the accuracy of detected tampered regions. We test the proposed methods using public tempered image database and our own data to verify their feasibility. The results also show that the proposed scheme outperforms existing ones in locating tampered areas.

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