Shaxun Chen

University of Michigan-Dearborn, Dearborn, Michigan, United States

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Publications (6)4.21 Total impact

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    ABSTRACT: Digital camera identification can be accomplished based on sensor pattern noise, which is unique to a device, and serves as a distinct identification fingerprint. Camera identification and authentication have formed the basis of image/video forensics in legal proceedings. Unfortunately, real-time video source identification is a computationally heavy task, and does not scale well to conventional software implementations on typical embedded devices. In this paper, we propose a hardware architecture for source identification in networked cameras. The underlying algorithms, an orthogonal forward and inverse discrete wavelet transform and minimum mean square error-based estimation, have been optimized for 2-D frame sequences in terms of area and throughput performance. We exploit parallelism, pipelining, and hardware reuse techniques to minimize hardware resource utilization and increase the achievable throughput of the design. A prototype implementation on a Xilinx Virtex-6 FPGA device was optimized with a resulting throughput of 167 MB/s, processing 30 640 × 480 video frames in 0.17 s.
    IEEE Transactions on Circuits and Systems for Video Technology 01/2014; 24(1):157-167. · 1.82 Impact Factor
  • Shaxun Chen, A. Pande, Kai Zeng, P. Mohapatra
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    ABSTRACT: Video source identification is very important in validating video evidence, tracking down video piracy crimes and regulating individual video sources. With the prevalence of wireless communication, wireless video cameras continue to replace their wired counterparts in security/surveillance systems and tactical networks. However, wirelessly streamed videos usually suffer from blocking and blurring due to inevitable packet loss in wireless transmissions. The existing source identification methods experience significant performance degradation or even fail to work when identifying videos with blocking and blurring. In this paper, we propose a method which is effective and efficient in identifying such wirelessly streamed videos. In addition, we also propose to incorporate wireless channel signatures and selective frame processing into source identification, which significantly improve the identification speed.
    INFOCOM, 2013 Proceedings IEEE; 01/2013
  • Shaxun Chen, Kai Zeng, P. Mohapatra
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    ABSTRACT: In cognitive radio networks, an attacker transmits signals mimicking the characteristics of primary signals, in order to prevent secondary users from transmitting. Such an attack is called primary user emulation (PUE) attack. TV towers and wireless microphones are two main types of primary users in white space. Existing work on PUE attack detection only focused on the first category. For the latter category, primary users are mobile and their transmission power is low. These properties introduce great challenges on PUE detection and existing methods are not applicable. In this paper, we propose a novel method to detect the emulation attack of wireless microphones. We exploit the relationship between RF signals and acoustic information to verify the existence of wireless microphones. The effectiveness of our approach is validated through real-world implementation. Extensive experiments show that our method achieves both false positive rate and false negative rate lower than 0.1 even in a noisy environment.
    IEEE Transactions on Mobile Computing 01/2013; 12(3):401-411. · 2.40 Impact Factor
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    Shaxun Chen, Kai Zeng, Prasant Mohapatra
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    ABSTRACT: Network forensics is widely used in tracking down criminals and detecting network anomalies, and data capture is the basis of network forensics. Compared to traditional networks, data capture faces significant challenges in cognitive radio networks. In traditional wireless networks, one monitor is usually assigned to one channel to capture traffic, which incurs very high cost in a cognitive radio network because the latter typically has a large number of channels. Furthermore, due to the uncertainty of the primary user's activity, cognitive radio devices change their operating channels randomly, which makes data capturing more difficult. In this paper, we propose a systematic method to capture data in cognitive radio networks with a small number of monitors. We utilize incremental support vector regression to predict packet arrival time and intelligently switch monitors between channels. In addition, a protocol is proposed to schedule multiple monitors to perform channel scan and packet capturing in an efficient manner. The real-world experiments and simulations show that our method is able to achieve the packet capture rate above 70% using a small number of monitors, which outperforms the random scheme by 200%–300%.
    Proceedings of the 19th annual IEEE International Conference on Network Protocols, ICNP 2011, Vancouver, BC, Canada, October 17-20, 2011; 01/2011
  • Source
    Shaxun Chen, Kai Zeng, Prasant Mohapatra
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    ABSTRACT: In cognitive radio networks, an adversary transmits signals whose characteristics emulate those of primary users, in order to prevent secondary users from transmitting. Such an attack is called primary user emulation (PUE) attack. There are two main types of primary users in white space: TV towers and wireless microphones. Existing work on PUE attack detection focused on the first category. However, for the latter category, primary users are mobile and their transmission power is low. These unique properties of wireless microphones introduce great challenges and existing methods are not applicable. In this paper, we propose a novel method to detect the PUE attack of mobile primary users. We exploit the correlations between RF signals and acoustic information to verify the existence of wireless mi­ crophones. The effectiveness of our approach is validated through extensive real-world experiments. It shows that our method achieves both false positive rate and false negative rate lower than 0.1.
    INFOCOM 2011. 30th IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies, 10-15 April 2011, Shanghai, China; 01/2011
  • Source
    Shaxun Chen, Kai Zeng, P. Mohapatra
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    ABSTRACT: Channel surfing is an effective method to prevent jamming attacks in wireless communications. In traditional channel surfing schemes, two parties have to negotiate beforehand, in order to agree on the channel switching sequence. However, the negotiation process itself is vulnerable to jamming attacks. In this paper, we propose a novel channel surfing method without relying on such negotiation. Taking advantage of the reciprocity of the wireless fading channel, our method switches channels according to the random channel states observed by the two parties during their communication. Therefore, it does not introduce any extra communication overhead and can achieve strong security. To evaluate our method, we carry out extensive experiments using off-the-shelf 802.11 devices in a real indoor environment. Experimental results validate the efficiency and security of our method.
    Communications (ICC), 2010 IEEE International Conference on; 06/2010

Publication Stats

5 Citations
4.21 Total Impact Points

Institutions

  • 2013
    • University of Michigan-Dearborn
      • Department of Computer & Information Science
      Dearborn, Michigan, United States
  • 2010–2013
    • University of California, Davis
      • Department of Computer Science
      Davis, California, United States