The present disclosure generally relates to forensic authentication of digital audio recordings.
An important task for forensic authentication of digital audio recordings is to determine whether the recordings have been tampered with. Unlike analog recordings, digital recordings may be altered using sophisticated editing software without leaving obvious signs of tampering. Since the signal characteristics of digital recordings are different from those of analog recordings, traditional methods for authenticating analog recordings fail for digital ones.
An electric network frequency (ENF) criterion has been shown to be a promising technique in detecting tampering of digital audio recordings. An ENF sequence may exist in some digital audio recordings when corresponding recording devices are mains-powered (e.g., directly connected to a utility power grid through a conventional outlet) or used in proximity of other mains-powered equipment even if the recording devices are battery-powered. Such recording devices capture not only the audio data but also, from the power grid, some 50/60-Hz sequence when mains-powered or 100/120-Hz sequence when battery-powered. The ENF criterion comprises extracting an ENF sequence from a recording and matching the ENF sequence against a frequency reference database to find the production time and tampering information, if any, of the recording. However, the reliability of the detection depends on the algorithm used to extract the ENF sequence.
It has also been shown that sudden changes in electric network phase angle sequences extracted from digital audio recordings may be used to detect tampering of the digital audio recordings without a phase angle reference database. However, disturbances in a power grid may occasionally cause sudden changes in the phase angle of the power grid. Such changes in phase angle caused by disturbances are very similar to those created by tampering of recordings, and thus may result in erroneous tampering detection.
Additionally, the capability of previous efforts to identify the source location of a recording is limited to the size of one interconnected grid. In other words, matching an ENF sequence and/or a phase angle sequence to a reference database is only capable of identifying the power grid interconnection (e.g., Eastern Interconnection (EI), Western Electricity Coordinating Council (WECC), Electric Reliability Council of Texas system (ERCOT)), but not the state, city, or location within a city, where the audio recording took place.
Therefore, the inventors recognized a need in the art for improving the reliability of tampering detection of digital audio recordings and better interpreting the results, and also identifying the source location of the digital audio recordings.
Embodiments of the present disclosure provide systems and methods for detecting a tampering and identifying a location of a digital recording. A frequency sequence and a phase angle sequence may be extracted from the digital recording. A portion of the frequency sequence may be matched to one of a plurality of reference frequency sequences, and a portion of the phase angle sequence may be matched to one of a plurality of reference phase angle sequences. Tampering of the digital recording may be detected when the frequency and phase sequences differ from the matched reference sequences. Moreover, a noise sequence may be extracted from the extracted frequency sequence. A location of the digital recording may be identified by matching the noise sequence to one of a plurality of noise sequences of the plurality of reference frequency sequences.
Further, at step 150 of the method 100, a noise sequence may be extracted from the frequency, which was extracted from the digital audio recording at step 120. At step 160, the extracted noise sequence may be matched against noise sequences of historical frequencies recorded and stored at locations within the major power grid interconnection, which corresponds to the reference database against which the digital audio recording matches. At step 170, based on the matching noise sequences, the method 100 may identify the location where the digital audio recording took place.
While the frequency across a major interconnection (e.g., WECC, EI, or ERCOT) is expected to be the same, the noise characteristics among states, cities, and different locations within a city are different due to varying loads, allowing for the location identification of digital audio recordings, as will described below. Although, the distribution of FDRs is shown for North America, the present invention may be applied to any power system worldwide. The FDRs collectively may form a monitoring network.
As discussed, to match a digital audio recording against one of the reference databases, an electric network frequency (ENF) sequence and phase angle sequence need to be extracted from the digital audio recording (e.g., at step 120 of the method 100 of
In equation (1), m=1, 2, . . . , (N−M)/P, k={1, 2, . . . , M}, w is a window function, and P is the size in each step, sometimes called the “hop size.” The STFT is a windowed Fourier transform wherein an analyzed signal is truncated by a moving window function.
Generally, in the frequency domain, an N-point DFT of a sinusoidal signal x(n) is a series of discrete samples X(k), which may be expressed as in equation (2).
In equation (2), k={0, . . . , N−1}, A is the amplitude, and θ is the initial phase. A coarse frequency of the signal corresponds to lfs/N, where fs is the sampling frequency. On right-hand side of equation (2), the first term represents the positive frequency component, while the second term is the negative frequency component. The frequency spectrum may be obtained using the STFT and k=kpeak may be found as in equation (3) to correspond to one of the samples X(k) having the largest magnitude.
kpeak=arg max|X| (3)
A fractional term δ (e.g., |δ|≤0.5) then may be calculated based on three DFT samples around and including the peak as in equation (4) to refine kpeak.
The real frequency may correspond to l=kpeak+δ. Three bins may be obtained as in equation (5) around the peak value by substituting kpeak into equation (2) and letting α=π(N−1)/N.
Since it may be shown that the amplitude of the positive frequency component is much larger than that of the negative frequency component, the negative frequency component may be neglected. Therefore, the amplitude A and the phase angle θ of the signal may be estimated according to the expression of X(kpeak) las in equation (6).
The coarse frequency then may be refined as in equation (7) to provide the frequency of the signal.
Since the ENF always occurs within a certain frequency range (e.g., around 50/60 Hz), to reduce the computation burden, in equation (2), k may be constrained to bins according to a preset frequency range of interest, for example [f1, f2]. Thus, an adjusted STFT may be represented as in equation (8).
Therefore, at step 120 of the method 100 illustrated in
After the ENF sequence is extracted from the digital audio recording, the ENF sequence may matched against the reference databases of the monitoring network discussed with respect to
In equation (9), M is the length of the extracted ENF and ref stands for a reference frequency sequence from one of the reference databases. M may be determined by the hop size. A smaller hop size may result in more frames and consequently a longer ENF sequence. A match may be determined when the MSE E is less than a predetermined threshold.
Similarly, at step 120 of the method 100, a phase angle sequence may be extracted from a digital audio recording using a DFT method, as discussed. At step 130, the extracted phase angle sequence may be matched against reference phase sequences. The starting time for the phase angle sequence matching may be obtained from the ENF matching.
Two typical types of tampering are usually of concern—deletion and replacement.
For different ENF and phase angle extraction methods and parameter settings, the ability of detecting tampering using frequency or phase angle may be different. For example,
On the other hand, in real power grids, there occasionally are sudden phase angle changes due to disturbances.
Once a frequency matching is obtained (e.g., in step 130 of the method 100), the major power grid interconnection (e.g., WECC, EI, or ERCOT) to which the digital audio recording belong may be known. Further, a location where the digital audio recording took place may be determined within the identified interconnection as discussed next.
Variations among ENF references within the same interconnection have been found to be caused by local load characteristics. While ENF references within the same interconnection follow the same trend, each ENF reference includes a background noise that is location-dependent and shows a unique statistical characteristic in the frequency domain. Therefore, to identify a location where a digital audio was recorded, a noise sequence may be extracted from the ENF sequence, which may be extracted from the digital audio recording (e.g., at step 120 of the method 100). The extracted noise sequence then may be matched against noise sequences of historical frequencies recorded by FDRs in the same interconnection.
As an alternative to using a neural network, correlation coefficients may be computed between a target frequency spectrum and reference frequency spectra. High correlation coefficients with respect to one frequency spectrum compared to other frequency spectra may typically indicate a match.
Thus, if a target frequency spectrum is obtained from the noise extracted from an ENF sequence of a digital audio recording, for example, the location of the digital audio recording may be identified by computing correlation coefficients between the target frequency of the digital audio recording and reference frequency spectra of noises from reference frequency sequences of a plurality of FDRs. Similarly, if a target frequency spectrum is extracted from a frequency sequence recorded by an FDR or any other phasor measurement unit, the frequency sequence may be authenticated, and this may allow for the detection of cyber-attacks on, for example, potentially critical power grid data.
As the number of FDRs increases, the likelihood of finding a match (i.e., the location where the digital audio was recorded) may increase, but the matching processes may take longer. However, the digital aspect of recordings and reference databases may allow for parallel processing, which may considerably speed up the matching processes. For example, an ENF and phase angle sequences extracted from a digital audio recording may be matched in parallel against each of a plurality of frequency and phase angle sequences from reference databases. To speed the matching processes even more, the frequency and phase angle sequences from the reference databases may be divided into a plurality of segments against which the digital audio recording may be matched in parallel.
Several embodiments of the disclosure are specifically illustrated and/or described herein. However, it will be appreciated that modifications and variations of the disclosure are covered by the above teachings and within the purview of the appended claims without departing from the spirit and intended scope of the disclosure. Further variations are permissible that are consistent with the principles described above.
This invention was made with government support under grants from the National Science Foundation, Award No. EEC-1041877, the Department of Justice, National Institutes of Justice, Award No. 2009-DN-BX-K233, and the Department of Energy, Award No. DE-AC05-00OR22725. The U.S. Government has certain rights in this invention.
Number | Name | Date | Kind |
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20150052073 | Panczenko | Feb 2015 | A1 |
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Number | Date | Country | |
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20170200457 A1 | Jul 2017 | US |