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SubmittedOpen Access (License Unspecified), Open
Conference proceeding
FORENSIC IDENTIFICATION OF COMPRESSIVELY SENSED SIGNALS
2012 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2012), pp 257-260
01 Jan 2012
Abstract
Identifying a signal's origin and how it was acquired is an important problem for digital forensics. Recently, compressive sensing has achieved substantial attention due to its ability to accurately acquire sparse signals at rates below the Nyquist rate. The increased popularity of this signal acquisition technique gives rise to a new forensic problem: is it possible to distinguish signals that have been compressively sensed from traditionally sampled ones? In our previous work, we addressed this problem of differentiating between traditionally acquired and compressively sensed images. In this paper, we examine the problem of distinguishing traditionally sampled signals from compressively sensed ones for a broader class of signals. We categorize those compressive sensing applicable signals into two cases: sparse signals with noise and nearly sparse signals. For each category, we discuss the traces left in a signal by compressive sensing and propose a corresponding detection scheme. Experimental results show that both of our proposed detection schemes can be effectively used to distinguish compressively sensed signals from traditionally sensed signals.
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Details
- Title
- FORENSIC IDENTIFICATION OF COMPRESSIVELY SENSED SIGNALS
- Creators
- Xiaoyu Chu - University of Maryland, College ParkMatthew C. Stamm - University of Maryland, College ParkK. J. Ray Liu - University of Maryland, College ParkIEEE
- Publication Details
- 2012 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2012), pp 257-260
- Series
- IEEE International Conference on Image Processing ICIP
- Publisher
- IEEE
- Number of pages
- 4
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000319334900061
- Scopus ID
- 2-s2.0-84875836798
- Other Identifier
- 991019295308804721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Imaging Science & Photographic Technology