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Compressive Sensing Forensics

Abstract: 

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. Additionally, 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 less than 10%. Additionally, our estimator for the number of compressive measurements can well reflect the real number.

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Paper Details

Authors:
Matthew C. Stamm
Submitted On:
23 February 2016 - 1:43pm
Short Link:
Type:
Research Manuscript

Document Files

double_AQ.pdf

(377 downloads)

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[1] Matthew C. Stamm, "Compressive Sensing Forensics", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/146. Accessed: Sep. 19, 2017.
@article{146-15,
url = {http://sigport.org/146},
author = {Matthew C. Stamm },
publisher = {IEEE SigPort},
title = {Compressive Sensing Forensics},
year = {2015} }
TY - EJOUR
T1 - Compressive Sensing Forensics
AU - Matthew C. Stamm
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/146
ER -
Matthew C. Stamm. (2015). Compressive Sensing Forensics. IEEE SigPort. http://sigport.org/146
Matthew C. Stamm, 2015. Compressive Sensing Forensics. Available at: http://sigport.org/146.
Matthew C. Stamm. (2015). "Compressive Sensing Forensics." Web.
1. Matthew C. Stamm. Compressive Sensing Forensics [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/146