Journal article
Compressive Sensing Forensics
IEEE transactions on information forensics and security, v 10(7), pp 1416-1431
Jul 2015
Abstract
Identifying a signal's origin and how it was acquired is an important forensic problem. While forensic techniques currently exist to determine a signal's acquisition history, these techniques do not account for the possibility that a signal could be compressively sensed. This is an important problem since compressive sensing techniques have seen increased popularity in recent years. In this paper, we propose a set of forensic techniques to identify signals acquired by compressive sensing. We do this by first identifying the fingerprints left in a signal by compressive sensing. We then propose two compressive sensing detection techniques that can operate on a broad class of signals. Since compressive sensing fingerprints can be confused with fingerprints left by traditional image compression techniques, we propose a forensic technique specifically designed to identify compressive sensing in digital images. In addition, we propose a technique to forensically estimate the number of compressive measurements used to acquire a signal. Through a series of experiments, we demonstrate that each of our proposed techniques can perform reliably under realistic conditions. Simulation results show that both our zero ratio detector and distribution-based detector yield perfect detections for all reasonable conditions that compressive sensing is used in applications, and the specific two-step detector for images can at least achieve probability of detection of 90% for probability of false alarm <;10%. In addition, our estimator for the number of compressive measurements can well reflect the real number.
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Details
- Title
- Compressive Sensing Forensics
- Creators
- Xiaoyu Chu - University of Maryland, College ParkMatthew Christopher Stamm - Drexel UniversityK. J. Ray Liu - University of Maryland, College Park
- Publication Details
- IEEE transactions on information forensics and security, v 10(7), pp 1416-1431
- Publisher
- IEEE
- Grant note
- FA95500910179 / Air Force Office of Scientific Research, Arlington, VA, USA CCF1320803 / National Science Foundation (10.13039/100000001)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000354956100009
- Scopus ID
- 2-s2.0-84930226592
- Other Identifier
- 991019174281004721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Theory & Methods
- Engineering, Electrical & Electronic