Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Detection of Lurkers in Online Social Networks Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Noisy Smoothing Image Source Identification

verfasst von : Yuying Liu, Yonggang Huang, Jun Zhang, Xu Liu, Hualei Shen

Erschienen in: Cyberspace Safety and Security

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Feature based image source identification plays an important role in the toolbox for forensics investigations on images. Conventional feature based identification schemes suffer from the problem of noise, that is, the training dataset contains noisy samples. To address this problem, we propose a new Noisy Smoothing Image Source Identification (NS-ISI) method. NS-ISI address the noise problem in two steps. In step 1, we employ a classifier ensemble approach for noise level evaluation for each training sample. The noise level indicates the probability of being noisy. In step 2, a noise sensitive sampling method is employed to sample training samples from original training set according to the noise level, producing a new training dataset. The experiments carried out on the Dresden image collection confirms the effectiveness of the proposed NS-ISI. When the noisy samples present, the identification accuracy of NS-ISI is significantly better than traditional methods.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel CloudDPI: Cloud-Based Privacy-Preserving Deep Packet Inspection via Reversible Sketch
Nächstes Kapitel Privacy-Preserving Comparable Encryption Scheme in Cloud Computing
Fußnoten
Literatur
1.
Stamm, M.C., Liu, K.J.R.: Forensic detection of image manipulation using statistical intrinsic fingerprints. IEEE Trans. Inf. Forensics Secur. 5(3), 492–506 (2010)CrossRef
2.
Celiktutan, O., Sankur, B., Avcibas, I.: Blind identification of source cell-phone model. IEEE Trans. Inf. Forensics Secur. 3(3), 553–566 (2008)CrossRef
3.
Jain, L.C., Pan, J.S.: [1]. In: Cox, I.J., Miller, M.L., Bloom, J.A. (eds.) Digital Watermarking and Fundamentals. Morgan Kaufmann, San Francisco (2002)
4.
Schyndel, R.G.V., Tirkel, A.Z., Osborne, C.F.: A digital watermark. In: Proceedings of IEEE International Conference on Image Processing, ICIP-94, vol. 2, pp. 86–90 (2002)
5.
Committee, C.: Exchangeable image file format for digital still cameras: Exif version 2.3. (2010)
6.
Bayram, S., Sencar, H., Memon, N., Avcibas, I.: Source camera identification based on CFA interpolation. In: IEEE International Conference on Image Processing, vol. 3, pp. 69–72 (2009)
7.
Kharrazi, M., Sencar, H.T., Memon, N.: Blind source camera identification (2004)
8.
Peng, F., Shi, J., Long, M.: Comparison and analysis of the performance of PRNU extraction methods in source camera identification. J. Comput. Inf. Syst. 9(14), 5585–5592 (2013)
9.
Castiglione, A., Cattaneo, G., Cembalo, M., Petrillo, U.F.: Experimentations with source camera identification and online social networks. J. Ambient Intell. Humanized Comput. 4(2), 265–274 (2013)CrossRef
10.
Cortes, C., Vapnik, V.: Support-Vector Networks. Kluwer Academic Publishers, Boston (1995)MATH
11.
Lyu, S., Farid, H.: Detecting hidden messages using higher-order statistics and support vector machines. In: Revised Papers from the International Workshop on Information Hiding, pp. 340–354 (2002)
12.
Wahid, A., Wahab, A., Ho, A.T.S., Li, S.: Inter-camera model image source identification with conditional probability features. In: Proceedings of IIEEJ Image Electronics & Visual Computing Workshop (2012)
13.
Wang, B., Kong, X., You, X.: Source camera identification using support vector machines. In: Peterson, G., Shenoi, S. (eds.) DigitalForensics 2009. IAICT, vol. 306, pp. 107–118. Springer, Heidelberg (2009). doi:10.​1007/​978-3-642-04155-6_​8 CrossRef
14.
Roychowdhury, A., Lin, T.Y., Maji, S., Learnedmiller, E.: Face identification with bilinear CNNs, pp. 1–9 (2015)
15.
Lukas, J., Fridrich, J., Goljan, M.: Digital camera identification from sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 1(2), 205–214 (2006)CrossRef
16.
Sutcu, Y., Bayram, S., Sencar, H.T., Memon, N.: Improvements on sensor noise based source camera identification. In: IEEE International Conference on Multimedia and Expo, pp. 24–27 (2007)
17.
Li, C.T.: Source camera identification using enhanced sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 5(2), 280–287 (2010)CrossRef
18.
Li, C.T., Li, Y.: Color-decoupled photo response non-uniformity for digital image forensics. IEEE Trans. Circ. Syst. Video Technol. 22(2), 260–271 (2012)CrossRefMathSciNet
19.
Kang, X., Li, Y., Qu, Z., Huang, J.: Enhancing source camera identification performance with a camera reference phase sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 7(2), 393–402 (2012)CrossRef
20.
Kang, X., Chen, J., Lin, K., Peng, A.: A context-adaptive spn predictor for trustworthy source camera identification. EURASIP J. Image Video Process. 2014(1), 19 (2014)CrossRef
21.
Tsai, M.J., Wang, C.S., Liu, J., Yin, J.S.: Using decision fusion of feature selection in digital forensics for camera source model identification. Comput. Stand. Interfaces 34(3), 292–304 (2012)CrossRef
22.
Choi, K.S., Lam, E.Y., Wong, K.K.: Automatic source camera identification using the intrinsic lens radial distortion. Opt. Express 14(24), 11551–11565 (2006)CrossRef
23.
Kai, S.C., Lam, E.Y., Wong, K.K.Y.: Source camera identification using footprints from lens aberration. In: Proceedings of SPIE - The International Society for Optical Engineering, vol. 6069, pp. 60690J–60690J-8 (2006)
24.
Min, G.H., Har, D.H., Park, H.J.: Source camera identification based on interpolation via lens distortion correction. Aust. J. Forensic Sci. 46(1), 98–110 (2014)CrossRef
25.
Van, L.T., Emmanuel, S., Kankanhalli, M.S.: Identifying source cell phone using chromatic aberration. In: IEEE International Conference on Multimedia and Expo, pp. 883–886 (2007)
26.
Dirik, A.E., Sencar, H.T., Memon, N.: Digital single lens reflex camera identification from traces of sensor dust. IEEE Trans. Inf. Forensics Secur. 3(3), 539–552 (2008)CrossRef
27.
Dirik, A.E., Sencar, H.T., Memon, N.: Source camera identification based on sensor dust characteristics. In: IEEE Workshop on Signal Processing Applications for Public Security and Forensics, SAFE 2007, pp. 1–6 (2007)
28.
Li, C.T., Li, Y.: Digital camera identification using colour-decoupled photo response non-uniformity noise pattern. In: IEEE International Symposium on Circuits and Systems, pp. 3052–3055 (2010)
29.
Liu, B.B., Lee, H.K., Hu, Y., Choi, C.H.: On classification of source cameras: a graph based approach. In: IEEE International Workshop on Information Forensics and Security, pp. 1–5 (2010)
30.
Li, C.T.: Unsupervised classification of digital images using enhanced sensor pattern noise. In: IEEE International Symposium on Circuits and Systems, pp. 3429–3432 (2010)
31.
Tuama, A., Comby, F., Chaumont, M.: Camera model identification with the use of deep convolutional neural networks. In: IEEE International Workshop on Information Forensics and Security (2016)
32.
Chen, Y.N., Han, C.C., Wang, C.T., Jeng, B.S., Fan, K.C.: The application of a convolution neural network on face and license plate detection. In: International Conference on Pattern Recognition, pp. 552–555 (2006)
33.
Wang, B., Zhang, J., Zhang, Z., Luo, W., Xia, D.: Robust traffic classification with mislabelled training samples. In: IEEE International Conference on Parallel and Distributed Systems, pp. 328–335 (2015)
34.
Pyun, K., Lim, J., Gray, R.M.: A robust hidden Markov Gauss mixture vector quantizer for a noisy source. IEEE Trans. Image Process. 18(7), 1385–1394 (2009). IEEE Signal Processing SocietyCrossRefMATHMathSciNet
35.
36.
Chatterjee, S., Dash, A., Bandopadhyay, S.: Ensemble support vector machine algorithm for reliability estimation of a mining machine. Qual. Reliab. Eng. Int. 31(8), 1503–1516 (2016)CrossRef
37.
Gloe, T.: The ‘Dresden image database’ for benchmarking digital image forensics. In: ACM Symposium on Applied Computing, pp. 1584–1590 (2010)
Metadaten
Titel
Noisy Smoothing Image Source Identification
verfasst von
Yuying Liu
Yonggang Huang
Jun Zhang
Xu Liu
Hualei Shen
Copyright-Jahr
2017
Buch
Cyberspace Safety and Security

Print ISBN: 978-3-319-69470-2

Electronic ISBN: 978-3-319-69471-9

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-69471-9_10