mirror of
https://github.com/bellingcat/open-questions.git
synced 2026-06-07 19:08:30 +03:00
List of possible used open source codes and important papes about the topic
This commit is contained in:
Radek Ludačka
78
electrical-network-frequency-analysis/papers.md
Normal file
78
electrical-network-frequency-analysis/papers.md
Normal file
@@ -0,0 +1,78 @@
|
||||
# Papers
|
||||
|
||||
###### Power Grid Estimation Using Electric Network Frequency Signals
|
||||
Using XGBoost classifier with 95.21% and 99.07% precision when ENF signals have 480 and 1920 data points
|
||||
https://www.hindawi.com/journals/scn/2019/1982168/
|
||||
|
||||
###### Location Forensics Analysis Using ENF Sequences Extracted from Power and Audio Recordings
|
||||
Using multi-class SVM classifier on audio recording with 81% precision of 60 Hz power grid audio samples and 77% precision of 50 Hz power grid audio samples.
|
||||
https://www.researchgate.net/publication/338064951_Location_Forensics_Analysis_Using_ENF_Sequences_Extracted_from_Power_and_Audio_Recordings
|
||||
|
||||
###### Detecting the Presence of ENF Signal in Digital Videos: a Superpixel based Approach
|
||||
In this paper, a superpixel based ENF detection algorithm for video is presented.
|
||||
Short video clips of about 2 minutes-length and can be used.
|
||||
The algorithm is able to operate independently of the source camera sensor type, CCD or CMOS and achieves a very high ENF signal presence detection accuracy for videos captured by both sensor types.
|
||||
90% true positive rate with 5% False positive rate. Full ROC curve in article.
|
||||
https://arxiv.org/pdf/1903.09884.pdf
|
||||
|
||||
###### Forensic analysis of digital audio recordings based on acoustic mains hum
|
||||
https://ieeexplore.ieee.org/document/7495982/metrics#metrics
|
||||
|
||||
###### Exploiting Spatial Signatures of Power ENF Signal for Measurement Source Authentication
|
||||
The maximum overall identification rate is above 95%. Using Random forest classifier. Locating power source from measured ENF in grid.
|
||||
https://www.osti.gov/servlets/purl/1558521
|
||||
|
||||
###### ENF Signal Induced by Power Grid: A New Modality for Video Synchronization
|
||||
Used ENF signal to synchronise video records from different recorded by different cameras located in the same house.
|
||||
https://www.mast.umd.edu/files/paper_ENF/conf_paper12_ACMMM14.pdf
|
||||
|
||||
###### ENF recognition article summary with brief method description.
|
||||
https://www.mast.umd.edu/research.php?t=enf
|
||||
|
||||
###### Information Forensics: An Overview of the First Decade - Anti-Forensics and Countermeasures.
|
||||
https://ieeexplore.ieee.org/document/6515027?tp=&arnumber=6515027&queryText%3Dinformation%20forensics%20overview%20of%20first%20decade=
|
||||
https://ieeexplore.ieee.org/document/6630051?tp=&arnumber=6630051&queryText%3Danti-forensics%20and%20countermeasures%20of=
|
||||
|
||||
###### “Seeing” ENF: Power-Signature-Based Timestamp for Digital Multimedia via Optical Sensing and Signal Processing
|
||||
An analytical model based on an autoregressive process is also developed for ENF signals, and the effectiveness of using innovation sequences from the model for timestamp verification is demonstrated.
|
||||
https://ieeexplore.ieee.org/document/6553242?tp=&arnumber=6553242&queryText%3Dpower%20signature%20based%20timestamp=
|
||||
|
||||
###### Spectrum Combining for ENF Signal Estimation.
|
||||
No audio. Video ENF detection used.
|
||||
https://ieeexplore.ieee.org/document/6557080?tp=&arnumber=6557080&queryText%3Dspectrum%20combining%20for%20enf=
|
||||
|
||||
###### Electric Network Frequency Analysis 2.0
|
||||
Using Fourier transformation with correlation coefficient of the frequency snippet.
|
||||
https://gridradar.net/en/blog/post/electric-network-frequency-analysis-20
|
||||
|
||||
###### Detecting Malicious False Frame Injection Attacks on Video Surveillance at the Edge using Electrical Network Frequency Signals
|
||||
https://www.preprints.org/manuscript/201904.0004/v1/download
|
||||
|
||||
###### Forensic Research Using Grid Data
|
||||
The basic approach to extract the 50/60Hz electric network frequency (or its harmonics) from the digital audio recordings and compare it against a reliable reference frequency database (here FNET database is used).
|
||||
https://powerit.utk.edu/forensic_research.html
|
||||
|
||||
###### VIDEO RECORDINGS LOCALIZATION BASED ON ELECTRIC NETWORK FREQUENCY VARIATION.
|
||||
With C and Matlab source code. Diploma thesis.
|
||||
http://castor.det.uvigo.es:8080/xmlui/bitstream/handle/123456789/182/Vlad-Dragos%20Darau.pdf?sequence=1&isAllowed=y
|
||||
|
||||
###### INVISIBLE GEO-LOCATION SIGNATURE IN A SINGLE IMAGE
|
||||
ENF method for image location detection.
|
||||
https://people.engr.ncsu.edu/cwong9/docs/2018_ICASSP.pdf
|
||||
|
||||
###### Feasibility of using Electrical Network Frequency fluctuations to perform forensic digital audio authentication
|
||||
C-program combined with a probe can be used to build the reference database.
|
||||
Short-Time Fourier Transform method is intended for the ENF extraction of longer signals while our novel proposed use of the Autoregressive parametric method and our implementation of the zero-crossing approach tackle the case of shorter recordings. A Graphical User Interface (GUI) was developed to facilitate the process of extracting the ENF fluctuations. The whole process is tested and evaluated for various scenarios ranging from long to short recordings.
|
||||
https://ruor.uottawa.ca/bitstream/10393/24383/3/El_Gemayel_Tarek_2013_thesis.pdf
|
||||
|
||||
###### Time-of-recording estimation for audio recordings.
|
||||
For example, compared with the recent DMA algorithm, our method achieves a relative error rate decrease of 86.86% (from 20.32% to 2.67%) and a speedup of 45× faster search response (41.0444 s versus 0.8973 s).
|
||||
https://www.sciencedirect.com/science/article/pii/S1742287617301883
|
||||
|
||||
###### Mechanisms estimation described in book.
|
||||
https://books.google.cz/books?id=_WcaEAAAQBAJ&pg=PA123&lpg=PA123&dq=enf+power+grid+audio+video+application&source=bl&ots=w4PxXL9MGS&sig=ACfU3U2Sh-94m41cPwdoCY61O-66SPHd3g&hl=cs&sa=X&ved=2ahUKEwijh8Dx5eXxAhUBvaQKHe3JDw8Q6AEwCXoECAoQAw#v=onepage&q=enf%20power%20grid%20audio%20video%20application&f=false
|
||||
|
||||
###### Geographic Location Estimation from ENF Signals with High Accuracy.
|
||||
80% Accuracy for ENF signal location estimation using SVM.
|
||||
http://home.ustc.edu.cn/~darksnip/Geographic%20Location%20Estimation%20from%20ENF%20Signals%20with%20High%20Accuracy.pdf
|
||||
|
||||
@@ -0,0 +1,16 @@
|
||||
# Available source codes
|
||||
|
||||
### PyEnf
|
||||
* Python based, ENF extraction from audio recordings.
|
||||
* Using harmonic frequencies and frequency spectorgram.
|
||||
|
||||
https://github.com/deerajnagothu/pyenf_extraction
|
||||
|
||||
### Assessing spectral estimation methods for Electric Network Frequency (ENF) Extraction.
|
||||
* Matlab source codes.
|
||||
* Signal filtering with a very sharp bandpass filter (main.m).
|
||||
* ENF extraction procedure using overplapping windows and a Quadratic Interpolation (main.m). Very good results achieved due to proper parameter tuning both in the pre-processing step and in the main procedure.
|
||||
* Georgios Karantaidis and Constantine Kotropoulos. 2018. "ASSESSING SPECTRAL ESTIMATION METHODS FOR ELECTRIC NETWORK FREQUENCY EXTRACTION",In 22nd Pan-Hellenic Conference on Informatics (PCI ’18), November 29-December 1, 2018, Athens, Greece. ACM, New York, NY, USA, 6 pages.
|
||||
|
||||
https://doi.org/10.1145/3291533.329153
|
||||
https://github.com/geokarant/ENF_extraction
|
||||
Reference in New Issue
Block a user