This book discusses blind investigation and recovery of digital evidence left behind on digital devices, primarily for the purpose of tracing cybercrime sources and criminals. It presents an overview of the challenges of digital image forensics, with a specific focus on two of the most common forensic problems. The first part of the book addresses image source investigation, which involves mapping an image back to its camera source to facilitate investigating and tracing the source of a crime. The second part of the book focuses on image-forgery detection, primarily focusing on “copy-move forgery” in digital images, and presenting effective solutions to copy-move forgery detection with an emphasis on additional related challenges such as blur-invariance, similar genuine object identification, etc. The book concludes with future research directions, including counter forensics. With the necessary mathematical information in every chapter, the book serves as a useful reference resource for researchers and professionals alike. In addition, it can also be used as a supplementary text for upper-undergraduate and graduate-level courses on “Digital Image Processing”, “Information Security”, “Machine Learning”, “Computer Vision” and “Multimedia Security and Forensics”.
| 1 Introduction |
|
1 | (10) |
|
1.1 Threats to the Integrity of Digital Media Content |
|
|
1 | (1) |
|
1.2 Digital Content Protection |
|
|
2 | (1) |
|
|
|
3 | (6) |
|
1.3.1 Image Source Identification |
|
|
3 | (1) |
|
1.3.2 Image Forgery Detection |
|
|
4 | (5) |
|
|
|
9 | (2) |
| 2 Camera Source Identification |
|
11 | (16) |
|
|
|
11 | (1) |
|
2.2 Digital Camera Components |
|
|
11 | (2) |
|
|
|
13 | (2) |
|
2.4 Source Camera Identification Framework |
|
|
15 | (5) |
|
2.4.1 Motivation for Choice of Features |
|
|
15 | (1) |
|
2.4.2 DCTR Feature Extraction |
|
|
15 | (2) |
|
2.4.3 Feature Transformation by PCA |
|
|
17 | (1) |
|
|
|
18 | (1) |
|
2.4.5 Ensemble Classifier |
|
|
19 | (1) |
|
|
|
20 | (5) |
|
|
|
20 | (2) |
|
2.5.2 Classification Accuracy Improvement (Dataset-1) |
|
|
22 | (1) |
|
2.5.3 Comparison of Accuracy with State-of-the-Art Techniques (Dataset-2) |
|
|
22 | (2) |
|
2.5.4 Evaluation of Overfitting Trends |
|
|
24 | (1) |
|
|
|
25 | (1) |
|
|
|
25 | (2) |
| 3 Copy-Move Forgery Detection in Digital Images-Survey and Accuracy Estimation Metrics |
|
27 | (30) |
|
|
|
27 | (1) |
|
3.2 Overview of Existing Techniques |
|
|
28 | (1) |
|
3.3 Classification of Block-Based Copy-Move Forgery Detection Techniques |
|
|
29 | (16) |
|
3.3.1 General Processing Pipeline for Copy-Move Forgery Detection Techniques |
|
|
30 | (1) |
|
3.3.2 Dimensionality Reduction-Based Copy-Move Forgery Detection |
|
|
30 | (5) |
|
3.3.3 Discrete Cosine Transform-Based Copy-Move Forgery Detection |
|
|
35 | (5) |
|
3.3.4 Wavelet Transform-Based Copy-Move Forgery Detection |
|
|
40 | (5) |
|
3.4 Three-Way Parameterization Platform |
|
|
45 | (2) |
|
|
|
47 | (8) |
|
|
|
47 | (1) |
|
3.5.2 Comparison of Detection Accuracy |
|
|
47 | (3) |
|
3.5.3 Comparison of False Positive Rate |
|
|
50 | (1) |
|
3.5.4 Comparison of False Negative Rate |
|
|
50 | (1) |
|
3.5.5 Trade-Off Between Detection Accuracy and Computational Complexity |
|
|
50 | (5) |
|
3.5.6 Trade-Off Between Detection Accuracy and False Positive and Negative Rates |
|
|
55 | (1) |
|
|
|
55 | (1) |
|
|
|
55 | (2) |
| 4 Copy-Move Forgery Detection Exploiting Statistical Image Features |
|
57 | (8) |
|
|
|
57 | (1) |
|
|
|
58 | (1) |
|
4.3 Region Duplication Detection Technique Using Statistical Image Features |
|
|
59 | (3) |
|
4.3.1 Reducing False Matches |
|
|
60 | (2) |
|
|
|
62 | (1) |
|
|
|
62 | (1) |
|
4.4.2 Comparison and Discussion |
|
|
62 | (1) |
|
|
|
63 | (1) |
|
|
|
63 | (2) |
| 5 Copy-Move Forgery Detection with Similar But Genuine Objects |
|
65 | (14) |
|
|
|
65 | (2) |
|
|
|
67 | (5) |
|
|
|
67 | (1) |
|
5.2.2 RLBP Feature Extraction |
|
|
68 | (2) |
|
|
|
70 | (1) |
|
5.2.4 Clustering and Forgery Detection |
|
|
71 | (1) |
|
|
|
72 | (4) |
|
|
|
72 | (1) |
|
5.3.2 Comparison with State of the Art |
|
|
72 | (3) |
|
5.3.3 Experiments on Post-processed Tampered Images |
|
|
75 | (1) |
|
|
|
76 | (1) |
|
|
|
76 | (3) |
| 6 Copy-Move Forgery Detection in Transform Domain |
|
79 | (8) |
|
|
|
79 | (1) |
|
6.2 DyWT-Based Image Region Duplication Detection |
|
|
79 | (4) |
|
6.2.1 Minimization of False Matches |
|
|
82 | (1) |
|
|
|
83 | (2) |
|
|
|
85 | (1) |
|
|
|
85 | (2) |
| 7 Conclusion and Future Research Directions |
|
87 | |
|
|
|
88 | |
Aniket Roy is an M.S. (research) student at the Department of Computer Science and Engineering at the Indian Institute of Technology Kharagpur. He received his B.Tech. degree in Electronics and Communication Engineering from West Bengal University of Technology (now Maulana Abul Kalam Azad University of Technology), India in 2014. His primary research interest lies in multimedia security, reversible watermarking and digital forensics. He received the Best Paper Award at the 15th International Workshop on Digital-forensics and Watermarking. He is an IEEE student member.
Rahul Dixit is a Ph.D. scholar at the Department of Computer Science and Engineering at the National Institute of Technology, Rourkela, India. He received his M.Tech. and B.Tech. degrees in Computer Science and Engineering from the Indian School of Mines, Dhanbad and Uttar Pradesh Technical University (now Dr. A.P.J. Abdul Kalam Technical University), respectively. His major research interests include digital image and video forensics, multimedia security and image processing.
Ruchira Naskar has been an assistant professor at the National Institute of Technology Department of Computer Science and Engineering, Rourkela, India since 2014. She received her Ph.D. from the Indian Institute of Technology Kharagpur, India in 2014. Her primary research interests are multimedia security and digital rights management, and she has over 30 publications in reputed journals and conferences. Her recent research interest is Digital Forensics. Dr. Naskar is a Member of the IEEE.
Rajat Subhra Chakraborty is an associate professor at the Indian Institute of Technology Kharagpur Department of Computer Science and Engineering, India. He has worked at National Semiconductor and Advanced Micro Devices (AMD). His research interests are in the areas of hardware security, very-large-scale integration (VLSI) design, digital watermarking and digital image forensics. He haspublished over 80 papers in respected international journals and conferences, and holds two U.S. patents. He has a Ph.D. in Computer Engineering from Case Western Reserve University (U.S.A.). Dr. Chakraborty is a senior member of the IEEE and the ACM.
Lisainfo e-raamatute kohta