A robust approach to detect digital forgeries by exploring correlation patterns

详细信息    查看全文

• 作者：Lu Li (1)
  Jianru Xue (1)
  Xiaofeng Wang (1) (2)
  Lihua Tian (1)
  
  1. Institute of Artificial Intelligence and Robotics ; Xi鈥檃n Jiaotong University ; No. 28 ; Xianning West Road ; Xi鈥檃n ; 710049 ; Shaanxi ; People鈥檚 Republic of China
  2. School of Science ; Xi鈥檃n University of Technology ; No. 5 ; South Jinhua Road ; Xi鈥檃n ; 710048 ; Shaanxi ; People鈥檚 Republic of China
  
• 关键词：Digital forgery ; Information security ; Color filter array ; Maximum posterior probability
• 刊名：Pattern Analysis & Applications
• 出版年：2015
• 出版时间：May 2015
• 年：2015
• 卷：18
• 期：2
• 页码：351-365
• 全文大小：2,156 KB
• 参考文献：1. Cao G, Zhao Y, Ni R, Tian H (2010) Anti-forensics of contrast enhancement in digital images. In: Proceedings of the 12th ACM workshop on multimedia and security, pp 25鈥?4
  2. Stamm, MC, Liu, KJR (2011) Anti-forensics of digital image compression. IEEE Trans Inf Forensics Secur 6: pp. 1050-1065 CrossRef
  3. Katzenbeisser S, Petitolas F (1999) Information hiding techniques for steganography and digital watermaking, 1st edn. Artech House, p 240, 10/1999 - ISBN: 1580530354
  4. Cox, I, Miller, M, Bloom, J, Honsinger, C (2002) Digital watermarking. J Electron Imaging 11: pp. 414-414 CrossRef
  5. Lu W, Varna AL, Wu M (2010) Forensic hash for multimedia information. SPIE Media Forensics and Security, pp. 7541鈥?Y
  6. Battiato, S, Farinella, GM, Messina, E, Puglisi, G (2012) Robust image alignment for tampering detection. IEEE Trans Inf Forensics Secur 7: pp. 1105-1117 CrossRef
  7. Farid, H (2009) A survey of image forgery detection. IEEE Signal Process Mag 2: pp. 16-25 CrossRef
  8. Mahdian, B, Saic, S (2010) A bibliography on blind methods for identifying image forgery. Signal Process Image Commun 25: pp. 389-399 CrossRef
  9. Redi, JA, Taktak, W, Dugelay, JL (2011) Digital image forensics: a booklet for beginner. Multimed Tools Appl 51: pp. 133-162 CrossRef
  10. Fridrich J, Soukal D, Luk谩拧 J (2003) Detection of copy-move forgery in digital images. In: Proceedings of digital forensic research workshop
  11. Popescu AC, Farid H (2004) Exposing digital forgeries by detecting duplicated image regions. Dartmouth College Tech. Rep, TR2004-515
  12. Mahdian, B, Saic, S (2007) Detection of copy-move forgery using a method based on blur moment invariants. Forensic Sci Int 171: pp. 180-189 CrossRef
  13. Luk谩拧 J, Fridrich J, Goljan M (2006) Detecting digital image forgeries using sensor pattern noise. In: Proceedings of SPIE 6072, pp 362鈥?72
  14. Hsu, YF, Chang, SF (2010) Camera response functions for image forensics: an automatic algorithm for splicing detection. IEEE Trans Inf Forensics Secur 5: pp. 816-825 CrossRef
  15. Popescu, AC, Farid, H (2005) Exposing digital forgeries by detecting traces of resampling. IEEE Trans Signal Process 53: pp. 758-767 CrossRef
  16. Popescu, AC, Farid, H (2005) Exposing digital forgeries in color filter array interpolated images. IEEE Trans Signal Process 53: pp. 3948-3959 CrossRef
  17. He J, Lin Z, Wang L, Tang X (2006) Detecting doctored JPEG images via DCT coefficient analysis. ECCV 2006, pp 423鈥?35
  18. Farid, H (2009) Exposing digital forgeries from JPEG ghosts. IEEE Trans Inf Forensics Secur 4: pp. 154-160 CrossRef
  19. Battiato S, Messina G (2009) Digital forgery estimation into DCT domain: a critical analysis. In: Proceedings of the first ACM workshop on multimedia in forensics 2009, pp 37鈥?2
  20. Zach F, Riess C, Angelopoulou E (2012) Automated Image Forgery Detection through Classification of JPEG Ghosts. Pattern Recognition 2012, pp. 185鈥?94
  21. Johnson MK, Farid H (2005) Exposing digital forgeries by detecting inconsistencies in lighting. In: Proceedings of the 7th workshop on multimedia and security, pp 1鈥?0
  22. Johnson, MK, Farid, H (2007) Exposing digital forgeries in complex lighting environments. IEEE Trans Inf Forensics Secur 2: pp. 450-461 CrossRef
  23. Gallagher AC, Chen T (2008) Image authentication by detecting traces of demosaicing. CVPRW鈥?8, pp 1鈥?
  24. Dirik AE, Memon N (2009) Image tamper detection based on demosaicing artifacts. ICIP鈥?009, pp 1497鈥?500
  25. Swaminathan, A, Wu, M, Liu, KJR (2007) Nonintrusive component forensics of visual sensors using output images. IEEE Trans Inf Forensics Secur 2: pp. 91-106 CrossRef
  26. Cao H, Kot AC (2009) Accurate detection of demosaicing regularity for digital image forensics. IEEE Trans Inf Forensics Secur 4(4):899鈥?10
  27. Fan N, Jin C, Huang Y (2009) A pixel-based digital photo authentication framework via demosaicking inter-pixel correlation. In: Proceedings of the 11th ACM workshop on multimedia and security, pp 125鈥?30
  28. Kirchner M (2010) Efficient estimation of CFA pattern configuration in digital camera images. In: Proceedings of SPIE, the International Society for Optical Engineering
  29. Takamatsu J, Matsushita Y, Ogasawara T, Ikeuchi K (2010) Estimating demosaicing algorithms using image noise variance. In: IEEE conference on computer vision and pattern recognition (CVPR), 2010, pp 279鈥?86
  30. Schaefer G, Stich M (2004) UCID鈥攁n uncompressed colour image database. In: Proceedings SPIE, storage and retrieval methods and applications for multimedia 2004, San Jose, USA, pp 472鈥?80
  31. Ng TT, Chang SF, Hsu J, Pepeljugoski M (2004) Columbia photographic images and photorealistic computer graphics dataset. Columbia University, ADVENT Technical Report, pp 205鈥?004
  32. Raph. http://www.raph.com. Accessed 28 March 2011
  33. 3dtotal. http://www.3dtotal.org. Accessed 28 March 2011
  34. Renderosity. http://www.renderosity.com. Accessed 28 March 2011
  35. Creative Crash. http://www.creativecrash.com. Accessed 28 March 2011
  36. 3dlinks. http://www.3dlinks.com/. Accessed 28 March 2011
  
• 刊物类别：Computer Science
• 刊物主题：Pattern Recognition
  
• 出版者：Springer London
• ISSN：1433-755X

文摘

Local correlation pattern indicates integrity of an image. Exposing digital forgeries by detecting local correlation pattern of images has become an important kind of approach among many others. However, local correlation pattern is sensitive to JPEG compression, since compression can be regarded as a local homogenization and attenuates the characteristics of local correlation pattern. In this paper, rather than concentrating on the differences between image textures which is common in previous works, we specifically build a gaussian model to describe the local-correlation pattern of color filter array (CFA) interpolation. Thus the model will automatically adapt to JPEG compression. With the model built from the test image, the posterior probability map of CFA interpolation is calculated. To measure the trace of CFA, frequency characteristics of the posterior probability map are calculated and weighted combined. Then the image is classified as tampered or not by a simple threshold. Experimental results from over thousands of tampered images show the validity and efficiency of the proposed method. Moreover, we examine our algorithm to the problem of distinguishing computer-generated images from photos and detecting local tamper area. The proposed method shows a good performance in these tests.