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Tampering Detection in Low-Power Smart Cameras

  • Adriano Gaibotti
  • Claudio Marchisio
  • Alexandro Sentinelli
  • Giacomo BoracchiEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 517)

Abstract

A desirable feature in smart cameras is the ability to autonomously detect any tampering event/attack that would prevent a clear view over the monitored scene. No matter whether tampering is due to atmospheric phenomena (e.g., few rain drops over the camera lens) or to malicious attacks (e.g., occlusions or device displacements), these have to be promptly detected to possibly activate countermeasures. Tampering detection is particularly challenging in battery-powered cameras, where it is not possible to acquire images at full-speed frame-rates, nor use sophisticated image-analysis algorithms.

We here introduce a tampering-detection algorithm specifically designed for low-power smart cameras. The algorithm leverages very simple indicators that are then monitored by an outlier-detection scheme: any frame yielding an outlier is detected as tampered. Core of the algorithm is the partitioning of the scene into adaptively defined regions, that are preliminarily defined by segmenting the image during the algorithm-configuration phase, and which shows to improve the detection of camera displacements. Experiments show that the proposed algorithm can successfully operate on sequences acquired at very low-frame rate, such as one frame every minute, with a very small computational complexity.

Keywords

Tampering detection Smart cameras Displacement detection Blurring detection Low-power cameras Low-frame rate 

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References

  1. 1.
    Hampapur, A., Brown, L., Connell, J., Ekin, A., Haas, N., Lu, M., Merkl, H., Pankanti, S.: Smart video surveillance: exploring the concept of multiscale spatiotemporal tracking. IEEE Signal Processing Magazine 22(2), 38–51 (2005)CrossRefGoogle Scholar
  2. 2.
    Aksay, A., Temizel, A., Cetin, A.E.: Camera tamper detection using wavelet analysis for video surveillance. In: IEEE Int. Conf. on Advanced Video and Signal Based Surveillance, AVVS 2007, pp. 558–562. IEEE (2007)Google Scholar
  3. 3.
    Saglam, A., Temizel, A.: Real-time adaptive camera tamper detection for video surveillance. In: Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2009, pp. 430–435. IEEE (2009)Google Scholar
  4. 4.
    Gil-Jiménez, P., López-Sastre, R.J., Siegmann, P., Acevedo-Rodríguez, J., Maldonado-Bascón, S.: Automatic control of video surveillance camera sabotage. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2007. LNCS, vol. 4528, pp. 222–231. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  5. 5.
    Tsesmelis, T., Christensen, L., Fihl, P., Moeslund, T.B.: Tamper detection for active surveillance systems. In: IEEE Int. Conf. on Advanced Video and Signal Based Surveillance, AVVS 2013, pp. 57–62 (2013)Google Scholar
  6. 6.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  7. 7.
    Ribnick, E., Atev, S., Masoud, O., Papanikolopoulos, N., Voyles, R.: Real-time detection of camera tampering. In: IEEE Int. Conf. on Video and Signal Based Surveillance, AVSS 2006, pp. 10–10 (2006)Google Scholar
  8. 8.
    Harasse, S., Bonnaud, L., Caplier, A., Desvignes, M.: Automated camera dysfunctions detection. In: IEEE Southwest Symp. on Image Analysis and Interpretation, pp. 36–40 (2004)Google Scholar
  9. 9.
    Komorkiewicz, T.K.M., Gorgon, M.: FPGA implementation of camera tamper detection in real-time. In: Int. Conf. on Design and Architectures for Signal and Image Processing DASIP, pp. 1–8 (2012)Google Scholar
  10. 10.
    Perrig, A., Stankovic, J., Wagner, D.: Security in wireless sensor networks. Communications of the ACM 47(6), 53–57 (2004)CrossRefGoogle Scholar
  11. 11.
    Alippi, C., Boracchi, G., Camplani, R., Roveri, M.: Detecting external disturbances on the camera lens in wireless multimedia sensor networks. IEEE Trans. on Instr. and Meas. 59(11), 2982–2990 (2010)CrossRefGoogle Scholar
  12. 12.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A survey. ACM Computing Surveys (CSUR) 41(3), 15 (2009)CrossRefGoogle Scholar
  13. 13.
    Gustafsson, F.: Adaptive Filtering and Change Detection. Wiley, October 2000Google Scholar
  14. 14.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), NovemberGoogle Scholar
  15. 15.
    Kottke, D.P., Sun, Y.: Motion estimation via cluster matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(11), 1128–1132 (1994)CrossRefGoogle Scholar
  16. 16.
    Caliński, T., Harabasz, J.: A dendrite method for cluster analysis. Communications in Statistics-theory and Methods 3(1), 1–27 (1974)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Adriano Gaibotti
    • 1
    • 2
  • Claudio Marchisio
    • 1
  • Alexandro Sentinelli
    • 1
  • Giacomo Boracchi
    • 2
    Email author
  1. 1.STMicroelectronics, Advanced System TechnologyAgrate BrianzaItaly
  2. 2.Dipartimento di Elettronica, Informazione E Bioingegneria (DEIB)Politecnico di MilanoMilanoItaly

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