High-performance combination method of electric network frequency and phase for audio forgery detection in battery-powered devices

Savari, Maryam; Wahab, Ainuddin Wahid Abdul; Anuar, Nor Badrul

doi:10.1016/j.forsciint.2016.07.001

Cited by 11 publications

(7 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Others were changed or simplified over time. Because language such an important form of communication, some people study other languages to be able to speak and write in them other than their own [14,15,16].…”

Section: Background Of the Studymentioning

confidence: 99%

Audio-based Assessment in Determining Language

Africa¹

2020

IJETER

View full text Add to dashboard Cite

The most apparent way humans communicate is verbal with the use of Language. With language being so complex, the average person would not be able to recognize another language besides their native own. But one thing is universal for all languages spoken throughout the world and it is that they produce sound. The average human being can hear sounds with frequencies ranging from 20 Hz to 20,000 Hz and with an intensity above the standard threshold of hearing. Hence, speaking produces sound with frequencies in between the said range. This paper aims to determine the language spoken of a given voice signal. By analyzing the given voice signal, its frequency range can be determined. Languages are spoken differently therefore have different frequency ranges. By using a filter to remove any ambient sounds from the input voice signal, its equivalent spectrogram reading gives an accurate range of its frequencies.

show abstract

Section: Background Of the Studymentioning

confidence: 99%

Audio-based Assessment in Determining Language

Africa¹

2020

IJETER

View full text Add to dashboard Cite

show abstract

“…2023, 13, 5039 2 of 16 power or located in environments where electromagnetic interference or acoustic mains hum is present [1,7,8]. This ENF signal can be estimated from the recordings using timedomain or frequency-domain techniques and utilized for various forensic and anti-forensic applications, such as time-stamp verification [5,8,9], audio/video authentication [10,11], location of recording estimation [12][13][14], power grid identification [15][16][17][18][19][20], and estimation of camera read-out time [21]. New studies have found that ENF analysis may be employed in other areas of multimedia signal processing, such as in audio and video record synchronization [22], historical audio recording alignment [23], and video synchronization without overlapping scenes [22].…”

Section: Introductionmentioning

confidence: 99%

Exploiting the Rolling Shutter Read-Out Time for ENF-Based Camera Identification

et al. 2023

View full text Add to dashboard Cite

The electric network frequency (ENF) is a signal that varies over time and represents the frequency of the energy supplied by a mains power system. It continually varies around a nominal value of 50/60 Hz as a result of fluctuations over time in the supply and demand of power and has been employed for various forensic applications. Based on these ENF fluctuations, the intensity of illumination of a light source powered by the electrical grid similarly fluctuates. Videos recorded under such light sources may capture the ENF and hence can be analyzed to extract the ENF. Cameras using the rolling shutter sampling mechanism acquire each row of a video frame sequentially at a time, referred to as the read-out time (Tro) which is a camera-specific parameter. This parameter can be exploited for camera forensic applications. In this paper, we present an approach that exploits the ENF and the Tro to identify the source camera of an ENF-containing video of unknown source. The suggested approach considers a practical scenario where a video obtained from the public, including social media, is investigated by law enforcement to ascertain if it originated from a suspect’s camera. Our experimental results demonstrate the effectiveness of our approach.

show abstract

“…The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of DARPA and the Air Force Research Laboratory (AFRL) or the U.S. Government including audio/video authentication [4], [5], [6], time stamp verification [7], [8], [9], and power grid identification [10].…”

Section: Introductionmentioning

confidence: 99%

“…This property of electric frequency, as well as the ability to extract it from multimedia files, has led to the exploitation of ENF in digital media forensics in recent years. It can be used for a variety of forensic and anti-forensic applications including audio/video authentication [4], [5], [6], time stamp verification [7], [8], [9], and power grid identification [10].…”

Section: Introductionmentioning

confidence: 99%

Detecting the Presence of ENF Signal in Digital Videos: A Superpixel-Based Approach

Vatansever

Dirik

Memon

2017

IEEE Signal Process. Lett.

View full text Add to dashboard Cite

ENF (Electrical Network Frequency) instantaneously fluctuates around its nominal value (50/60 Hz) due to a continuous disparity between generated power and consumed power. Consequently, luminous intensity of a mains-powered light source varies depending on ENF fluctuations in the grid network. Variations in the luminance over time can be captured from video recordings and ENF can be estimated through content analysis of these recordings. In ENF based video forensics, it is critical to check whether a given video file is appropriate for this type of analysis. That is, if ENF signal is not present in a given video, it would be useless to apply ENF based forensic analysis. In this work, an ENF signal presence detection method is introduced for videos. The proposed method is based on multiple ENF signal estimations from steady superpixels, i.e. pixels that are most likely uniform in color, brightness, and texture, and intraclass similarity of the estimated signals. Subsequently, consistency among these estimates is then used to determine the presence or absence of an ENF signal in a given video. The proposed technique can operate on video clips as short as 2 minutes and is independent of the camera sensor type, i.e. CCD or CMOS.

show abstract

High-performance combination method of electric network frequency and phase for audio forgery detection in battery-powered devices

Cited by 11 publications

References 14 publications

Audio-based Assessment in Determining Language

Audio-based Assessment in Determining Language

Exploiting the Rolling Shutter Read-Out Time for ENF-Based Camera Identification

Detecting the Presence of ENF Signal in Digital Videos: A Superpixel-Based Approach

Contact Info

Product

Resources

About