Advertisement

Discrimination of Computer Synthesized or Recaptured Images from Real Images

  • Tian-Tsong Ng
  • Shih-Fu Chang
Chapter

Abstract

An image that appears to be a photograph may not necessarily a normal photograph as we know it. For example, a photograph-like image can be rendered by computer graphics instead of being taken by a camera or it can be a photograph of an image instead of a direct photograph of a natural scene. What is really different between these photographic appearances is their underlying synthesis processes. Not being able to distinguish these images poses real social risks, as it becomes harder to refute claims of child pornography as non-photograph in the court of law and easier for attackers to mount an image or video replay attack on biometric security systems. This motivates digital image forensics research on distinguishing these photograph-like images from true photographs. In this chapter, we present the challenges, technical approaches, system design and other practical issues in tackling this multimedia forensics problem. We will also share a list of open resources and the potential future research directions in this area of research which we hope readers will find useful.

Keywords

Computer Graphic Support Vector Machine Classifier Equal Error Rate Photographic Image Child Pornography 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Athitsos V, Swain MJ, Frankel C (1997) Distinguishing photographs and graphics on the world wide web. In: IEEE Workshop on Content-Based Access of Image and Video Libraries, pp 10–17Google Scholar
  2. 2.
    Bai J, Ng T-T, Gao X, Shi Y-Q (2010) Is physics-based liveness detection truly possible with a single image? In: Proceedings of the IEEE International Symposium on Circuits and SystemsGoogle Scholar
  3. 3.
    Cao H, Kot AC (2010) Identification of recaptured photographs on LCD screens. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal ProcessingGoogle Scholar
  4. 4.
    Caruana R (1997) Multitask learning. Mach Learn 28(1):41–75MathSciNetCrossRefGoogle Scholar
  5. 5.
    Chen W, Shi Y-Q, Xuan G (2007) Identifying computer graphics using HSV color model and statistical moments of characteristic functions. In: Proceedings of the IEEE International Conference on Multimedia and Expo, pp 1123–1126Google Scholar
  6. 6.
    Chen Y, Li Z, Li M, Ma WY (2006) Automatic classification of photographs and graphics. In: Proceedings of the IEEE International Conference on Multimedia and Expo, pp 973–976Google Scholar
  7. 7.
    Choudhury T, Clarkson B, Jebara T, Pentland A (1999) Multimodal person recognition using unconstrained audio and video. In: International Conference on Audio- and Video-Based Person Authentication, pp 176–181Google Scholar
  8. 8.
    Dana KJ, Van Ginneken B, Nayar SK, Koenderink JJ (1999) Reflectance and texture of real-world surfaces. ACM Trans Graph 18(1):34CrossRefGoogle Scholar
  9. 9.
    Dehnie S, Sencar T, Memon N. Digital image forensics for identifying computer generated and digital camera images. In: Proceedings of the IEEE International Conference on Image Processing, pp 2313–2316Google Scholar
  10. 10.
    Dirik AE, Bayram S, Sencar HT, Memon N (2007) New features to identify computer generated images. In: Proceedings of the IEEE International Conference on Image Processing, vol 4, pp 433–436Google Scholar
  11. 11.
    Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley-Interscience, HobokenGoogle Scholar
  12. 12.
    Everingham M, Zisserman A, Williams C, Van Gool L, Allan M, Bishop C, Chapelle O, Dalal N, Deselaers T, Dorko G (2006) The 2005 PASCAL visual object classes challenge. Mach Learn Chall, 117–176Google Scholar
  13. 13.
    Farid H (2004) Creating and detecting doctored and virtual images: Implications to the child pornography prevention act. Technical report, TR2004-518, Dartmouth College, Computer ScienceGoogle Scholar
  14. 14.
    Farid H, Bravo MJ (2011) Perceptual discrimination of computer generated and photographic faces. Digital InvestigationGoogle Scholar
  15. 15.
    Farid H, Lyu S (2003) Higher-order wavelet statistics and their application to digital forensics. In: IEEE Workshop on Statistical Analysis in Computer VisionGoogle Scholar
  16. 16.
    Fei-Fei L, Fergus R, Perona P (2004) Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In: Workshop on Generative-Model Based VisionGoogle Scholar
  17. 17.
    Fergus R, Perona P, Zisserman PA (2003) Object class recognition by unsupervised scale-invariant learning. In: Proceedings of the Computer Vision and Pattern RecognitionGoogle Scholar
  18. 18.
    Ferwerda JA (2003) Three varieties of realism in computer graphics. In: Proceedings of the SPIE Human Vision and Electronic, Imaging, vol 3Google Scholar
  19. 19.
    Gallagher AC, Chen T (2008) Image authentication by detecting traces of demosaicing. In: Proceedings of the Computer Vision and, Pattern RecognitionGoogle Scholar
  20. 20.
    Gao X, Ng T-T, Qiu B, Chang S-F (2010) Single-view recaptured image detection based on physics-based features. In Proceedings of the IEEE International Conference on Multimedia and ExpoGoogle Scholar
  21. 21.
    Gao X, Qiu B, Shen J, Ng T-T, Shi Y-Q (2010) A smart phone image database for single image recapture detection. In Proc. of International Workshop on Digital WatermarkingGoogle Scholar
  22. 22.
    Grassberger P (1983) Generalized dimensions of strange attractors. Phys Lett A 97(6):227–230MathSciNetCrossRefGoogle Scholar
  23. 23.
    Grossberg M, Nayar SK (2003) What is the space of camera response functions? In: Proceedings of the Computer Vision and, Pattern RecognitionGoogle Scholar
  24. 24.
    Hartley R, Zisserman A (2000) Multiple view geometry, chapter 6, p 164. Cambridge university press, CambridgeGoogle Scholar
  25. 25.
    Healey G, Kondepudy R (1994) Radiometric CCD camera calibration and noise estimation. IEEE Trans Pattern Anal Mach Intell 16(3):267–276CrossRefGoogle Scholar
  26. 26.
    Ianeva TI, de Vries AP, Rohrig H (2003) Detecting cartoons: A case study in automatic video-genre classification. In: Proceedings of the IEEE International Conference on Multimedia and Expo, vol 1Google Scholar
  27. 27.
    Kajiya JT (1986) The rendering equation. In: Proceedings of the ACM SIGGRAPH, pp 143–150Google Scholar
  28. 28.
    Kam AH, Ng T-T, Kingsbury NG, Fitzgerald WJ (2000) Content based image retrieval through object extraction and querying, In: IEEE Workshop on Content-based Access of Image and Video LibrariesGoogle Scholar
  29. 29.
    Khanna N, Chiu GTC, Allebach JP, Delp EJ (2008) Forensic techniques for classifying scanner, computer generated and digital camera images. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp 1653–1656Google Scholar
  30. 30.
    Kharrazi M, Sencar HT, Memon N (2005) Benchmarking steganographic and steganalysis techniques, In: Proceedings of the SPIE Electronic ImagingGoogle Scholar
  31. 31.
    Kivinen J, Smola AJ, Williamson RC (2004) Online learning with kernels. IEEE Trans Signal Process 52(8):2165–2176MathSciNetCrossRefGoogle Scholar
  32. 32.
    Kollreider K, Fronthaler H, Bigun J (2009) Non-intrusive liveness detection by face images. Image Vis Comput 27(3):233–244CrossRefGoogle Scholar
  33. 33.
    Kovach S (2011) This guy just exposed a major security flaw in ice cream sandwich. http://www.businessinsider.com/ice-cream-sandwich-face-unlock-2011-11
  34. 34.
    Lalonde JF, Efros AA (2007) Using color compatibility for assessing image realism. In: Proceedings of the International Conference on Computer VisionGoogle Scholar
  35. 35.
    Li J, Wang Y, Tan T, Jain AK (2004) Live face detection based on the analysis of fourier spectra. In: Proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol 5404. pp 296–303Google Scholar
  36. 36.
    Lienhart R, Hartmann A (2002) Classifying images on the web automatically. J Electron Imaging 11(4):445–454CrossRefGoogle Scholar
  37. 37.
    Lukáš J, Fridrich J, Goljan M (2006) Digital camera identification from sensor pattern noise. IEEE Trans Inf Secur Forensics 16(3):205–214CrossRefGoogle Scholar
  38. 38.
    Lyu S, Farid H (2005) How realistic is photorealistic? IEEE Trans Signal Process 53(2):845–850MathSciNetCrossRefGoogle Scholar
  39. 39.
    Magee DR, Boyle RD (2002) Detecting lameness using re-sampling condensation and multi-stream cyclic hidden markov models. Image Vis Comput 20(8):581–594CrossRefGoogle Scholar
  40. 40.
    Mallat SG (1989) A theory for multiresolution signal decomposition: The wavelet representation. IEEE trans pattern anal mach intell 11(7):674–693zbMATHCrossRefGoogle Scholar
  41. 41.
    Mallick S, Zickler T, Belhumeur P, Kriegman D (2006) Specularity removal in images and videos: A pde approach. In: Proceedings of European Conference on Computer Vision, 550–563Google Scholar
  42. 42.
    Mandelbrot BB (1982) The fractal geometry of nature. W. H. Freeman, San FranciscoGoogle Scholar
  43. 43.
    Mayer GW, Rushmeier HE, Cohen MF, Greenberg DP, Torrance KE (1986) An experimental evaluation of computer graphics imagery. In: Proceedings of the ACM SIGGRAPH, pp 30–50Google Scholar
  44. 44.
    McNamara A (2005) Exploring perceptual equivalence between real and simulated imagery. In: Proceedings of the ACM symposium on Applied perception in graphics and visualization, p 128Google Scholar
  45. 45.
    Michels J, Saxena A, Ng AY (2005) High speed obstacle avoidance using monocular vision and reinforcement learning. In: Proceedings of the International Conference on Machine Learning, pp 593–600Google Scholar
  46. 46.
    Miller G, Hoffman CR (1984) Illumination and reflection maps: Simulated objects in simulated and real environments. In: SIGGRAPH 84 Advanced Computer Graphics Animation seminar notes, vol 190Google Scholar
  47. 47.
    Ng T-T, Chang S-F (2004) Classifying photographic and photorealistic computer graphic images using natural image statistics. Technical report, ADVENT Technical Report, 220-2006-6, Columbia UniversityGoogle Scholar
  48. 48.
    Ng T-T, Chang S-F (2006) An online system for classifying computer graphics images from natural photographs. In: Proceedings of SPIE, 6072, pp. 397–405, 2006. http://apollo.ee.columbia.edu/trustfoto/trustfoto/natcgV4.html
  49. 49.
    Ng T-T, Chang S-F, Hsu J, Xie L, Tsui M-P (2005) Physics-motivated features for distinguishing photographic images and computer graphics. In: Proceedings of the ACM International Conference on Multimedia, pp 239–248Google Scholar
  50. 50.
    Ng T-T, Chang S-F, Hsu Y-F, Pepeljugoski M (2004) Columbia photographic images and photorealistic computer graphics dataset. Technical report, ADVENT Technical Report, 203-2004-3, Columbia UniversityGoogle Scholar
  51. 51.
    Ng T-T, Chang S-F, Lin C-Y, Sun Q (2006) Passive-blind image forensics. In: Multimedia Security Technologies for Digital Rights, pp 111–137. ElsevierGoogle Scholar
  52. 52.
    Ng T-T, Chang S-F, Tsui M-P (2007) Lessons learned from online classification of photorealistic computer graphics and photographs. In: IEEE Workshop on Signal Processing Applications for Public Security and ForensicsGoogle Scholar
  53. 53.
    Ng T-T, Tsui M-P (2009) Camera response function signature for digital forensics—part I: Theory and data selection. In: IEEE Workshop on Information Forensics and Security (WIFS)Google Scholar
  54. 54.
    Ngo D (2008) Vietnamese security firm: Your face is easy to fake. http://news.cnet.com/8301-17938_105-10110987-1.html
  55. 55.
    Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987CrossRefGoogle Scholar
  56. 56.
    Pan F, Chen JB, Huang JW (2009) Discriminating between photorealistic computer graphics and natural images using fractal geometry. Science in China Series F: Information Sciences 52(2):329–337zbMATHCrossRefGoogle Scholar
  57. 57.
    Pan G, Sun L, Wu ZH, Lao SH (2007) Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In: Proceedings of the International Conference on Computer Vision, pp 1–8Google Scholar
  58. 58.
    Popescu AC, Farid H (2005) Exposing digital forgeries by detecting traces of resampling. IEEE Trans Signal Process 53(2):758–767MathSciNetCrossRefGoogle Scholar
  59. 59.
    Rademacher P, Lengyel J, Cutrell E, Whitted T (2001) Measuring the perception of visual realism in images. In: Proceedings of the Eurographics Workshop on Rendering, Techniques, pp 235–248Google Scholar
  60. 60.
    Raskar R, Tumblin J, Mohan A, Agrawal A, Li AY (2006) Computational photography. Proceedings of the Eurographics State of the Art ReportGoogle Scholar
  61. 61.
    Rocha A, Goldenstein S (2006) Is it fake or real?. In: XIX Brazilian Symposium on Computer Graphics and Image Processing, pp 1–2Google Scholar
  62. 62.
    Rui Y, Huang TS, Chang S-F (1999) Image retrieval: Current techniques, promising directions, and open issues. J vis commun image represent 10(1):39–62CrossRefGoogle Scholar
  63. 63.
    Sankar G, Zhao V, Yang YH (2009) Feature based classification of computer graphics and real images. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp 1513–1516Google Scholar
  64. 64.
    Shi YQ, Xuan G, Zou D, Gao J, Yang C, Zhang Z, Chai P, Chen W, Chen C (2005) Image steganalysis based on moments of characteristic functions using wavelet decomposition, prediction-error image, and neural network. In: Proceedings of the IEEE International Conference on Multimedia and ExpoGoogle Scholar
  65. 65.
    Simoncelli EP, Olshausen BA (2001) Natural image statistics and neural representation. Annu review neurosci 24(1):1193–1216CrossRefGoogle Scholar
  66. 66.
    Srivastava A, Lee AB, Simoncelli EP, Zhu SC (2003) On advances in statistical modeling of natural images. J Math Imaging vis 18(1):17–33MathSciNetzbMATHCrossRefGoogle Scholar
  67. 67.
    Sutthiwan P, Cai X, Shi Y-Q, Zhang H (2009) Computer graphics classification based on markov process model and boosting feature selection technique. In: Proceedings of the IEEE International Conference on Image Processing, pp 2877–2880Google Scholar
  68. 68.
    Sutthiwan P, Ye J, Shi Y-Q (2009) An enhanced statistical approach to identifying photorealistic images. Digit Watermark, 323–335Google Scholar
  69. 69.
    Tan RT, Ikeuchi K (2005) Separating reflection components of textured surfaces using a single image. IEEE Trans Pattern Anal Mach Intell 27(2):178–193CrossRefGoogle Scholar
  70. 70.
    Torralba A, Murphy KP, Freeman WT (2007) Sharing visual features for multiclass and multiview object detection. IEEE Trans Pattern Anal Mach Intell, 854–869Google Scholar
  71. 71.
    Umeyama S, Godin G (2004) Separation of diffuse and specular components of surface reflection by use of polarization and statistical analysis of images. IEEE Trans Pattern Anal Mach Intell 26(5):639–647CrossRefGoogle Scholar
  72. 72.
    Wang F, Kan MY (2006) NPIC: Hierarchical synthetic image classification using image search and generic features. Image Video Retr, 473–482Google Scholar
  73. 73.
    Wang N, Doube W (2011) How real is really? a perceptually motivated system for quantifying visual realism in digital images. In: Proceedings of the IEEE International Conference on Multimedia and Signal Processing 2:141–149Google Scholar
  74. 74.
    Wang Y, Moulin P (2006) On discrimination between photorealistic and photographic images. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing 2:161–164Google Scholar
  75. 75.
    Westfeld A, Pfitzmann A (2000) Attacks on steganographic systems. In: Information Hiding, pp 61–76. SpringerGoogle Scholar
  76. 76.
    Wu J, Kamath MV, Poehlman S (2006) Color texture analysis in distinguishing photos with computer generated images. In: Proceedings of the UW and IEEE Kitchener-Waterloo Section Joint Workshop on Knowledge and Data MiningGoogle Scholar
  77. 77.
    Wu J, Kamath MV, Poehlman S (2006) Detecting differences between photographs and computer generated images. In: Proceedings of the 24th IASTED international conference on Signal processing, pattern recognition, and applications, pp 268–273Google Scholar
  78. 78.
    Yu H, Ng T-T, Sun Q (2008) Recaptured photo detection using specularity distribution. In: Proceedings of the IEEE International Conference on Image Processing, pp 3140–3143Google Scholar
  79. 79.
    Zhang R, Wang R, Ng T-T (2011) Distinguishing photographic images and photorealistic computer graphics using visual vocabulary on local image edges. In: Proceedings of the International Workshop on Digital-forensics and WatermarkingGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Institute for Infocomm ResearchSingaporeSingapore
  2. 2.Columbia UniversityNew YorkUSA

Personalised recommendations