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Penn N. Markham

dblp:98/10731 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2013
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Digital forensics and information hiding · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Digital forensics and information hiding › digital forensics › multimedia forensics
audio forensics
0.212013
An Improved Discrete Fourier Transform-Based Algorithm for Electric Network Frequency Extraction · IEEE Trans. Inf. Forensics Secur. 2013
Digital forensics and information hiding › content authentication
audio authentication
0.012013
An Improved Discrete Fourier Transform-Based Algorithm for Electric Network Frequency Extraction · IEEE Trans. Inf. Forensics Secur. 2013

Methods — techniques the papers use, named apart from their topics

discrete fourier transform · 0.2binary search · 0.2
YearPublicationVenuePosition
2013 An Improved Discrete Fourier Transform-Based Algorithm for Electric Network Frequency Extraction
abstract
This paper introduces a Discrete Fourier Transform (DFT)-based algorithm to extract the Electric Network Frequency (ENF) information from an audio recording for use in audio authentication. The basic idea of the proposed algorithm is to calculate the specific spectral lines by DFT in the frequency domain at the desired frequency point instead of throughout the entire frequency band. Then a binary search technique is employed to search the next desired frequency bin to repeat the spectral line calculation until the hidden ENF information is extracted. The purpose is to improve the accuracy and precision of conventional ENF extraction methods and also to enhance the calculation efficiency. Both simulated audio signals with different signal-to-noise ratios (SNRs) and actual audio recordings are studied to verify the performance of the proposed algorithm. Two error-evaluation criteria, frequency offset and frequency bias, are defined to evaluate the algorithm performance on accuracy and precision. The test results and the error evaluation prove the validation and demonstrate the improvement of the proposed algorithm.
Penn N. Markham, Richard W. Conners, Yilu Liu 0001
IEEE Trans. Inf. Forensics Secur.2