臺灣博碩士論文加值系統

由於JPEG影像壓縮技術普遍被使用，今日，在討論數位影像竄改議題時，對於JPEG儲存格式影像的研究早已不可或缺。一般而言，在壓縮影像上進行人為竄改，通常會牽涉重新壓縮(recompression)的步驟。此步驟會消除竄改區塊上未壓縮前的特徵，提高偵測困難性。但相對地，我們亦可以透過重新壓縮的特性分析應用於竄改偵測。本論文中，重點將放在偵測JPEG儲存格式的來源影像是否經過竄改。首先，利用壓縮影像在空間(spatial domain)與頻域(DCT domain)上的壓縮特徵(compression artifacts)，我們提出有效的週期性分析方法來判斷一張影像是否被重新壓縮。接著，透過提出的壓縮雜訊模型(quantization noise model)以及影像還真技術(image restoration techniques)，進一步偵測出被竄改的局部區塊。實驗數據顯示，無論是偵測重新壓縮影像或者局部竄改偵測，我們提出的方法與模型的準確性都超越現有研究。此外，實驗同樣顯示在我們提出的影像還真技術(image restoration techniques)下，局部竄改偵測可以達到滿意的效果。

Since JPEG has been a popularly used image compression standard, tampering detection in JPEG images now plays an important role. Tampering on compressed images often involve recompression and tend to erase those tampering traces existed in uncompressed images. We could, however, try to discover new traces caused by recompression and use these traces to detect the recompression tampering. The artifacts introduced by lossy JPEG compression can be seen as an inherent signature for recompressed images. In this thesis, we first propose a robust tampered image detection approach by periodicity analysis with the compression artifacts both in spatial and DCT domain. To locate the forged regions, we then propose a forged regions localization method via quantization noise model and image restoration techniques. Finally, we conduct a series of experiments to demonstrate the validity of the proposed periodic features and quantization noise model, which all outperform the existing methods. Also, we show the effectiveness and feasibility of our forged regions localization method with proposed image restoration techniques.

誌　謝 I
中文摘要 II
Abstract III
1. Introduction 1
2. Related Work and Motivation 3
2.1 Tamper Detection by Inconsistency on Quantization Table 3
2.2 Compression Artifacts Detection in Spatial Domain 5
2.3 Compression Artifacts Detection in DCT Domain 6
2.3.1 A Generalized Benford’s Law for DCT coefficients 6
2.3.2 JPEG ghosts for Digital forgery 7
2.4 Motivation 8
3. Tampering Detection via Blocking Periodicity Analysis 15
3.1 Characteristics Analysis of JPEG Images 15
3.1.1 Periodicity of Blocking Artifacts 16
3.1.2 Periodicity of DCT coefficients 17
3.2 Characteristics Variation after Tampering 18
3.2.1 Periodicity Variation of Blocking artifacts 19
3.2.2 Periodicity Variation of DCT coefficients 21
3.3 Tampering Detection via JPEG Periodic Characteristics 23
3.3.1 Composite JPEG sources detection by Blocking artifacts 23
3.3.2 Composite JPEG Sources Detection by DCT coefficients 27
4. Locating Forged Regions via Quantization Noise Model 33
4.1 Quantization Noise Model 33
4.2 Ground Truth Estimation Based on Image Restoration 37
4.2.1 Deblocking 38
4.2.2 Low Frequency Compensation 39
4.2.3 Modification of Quantization Noise Model 40
5. Experimental Results 45
5.1 Validation of Periodic Features 45
5.1.1 Detecting Cropped and Recompressed Images 46
5.2.2 Detecting Composite JPEG sources 47
5.2 Robustness of Quantization Noise Model 48
5.3 Forgery Detection Based on Image Restoration 50
6. Conclusion and Future Work 59
7. References 60

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