Summary EarSpy Spying Caller Speech and Identity arxiv.org
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EarSpy is a technology that utilizes smartphone ear speaker vibrations and motion sensors to eavesdrop on caller speech and identity, while phone manufacturers have started incorporating larger speakers at the top of smartphones instead of the traditional ear speaker.
Slides
Slide Presentation (11 slides)
Key Points
- EarSpy is a system that can spy on caller speech and identity by capturing vibrations of smartphone ear speakers.
- Motion sensors can detect speech injected into loudspeaker reverberation, and eavesdropping on ear speakers is possible.
- Phone manufacturers use larger speakers at the top of smartphones instead of ear speakers, but the volume is controlled to prevent discomfort during phone conversations.
- Vibrations caused by sound transmitted through a smartphone's body can be captured by the motion sensor.
- The feasibility of playing voice through ear speakers is determined using third-party Android apps.
- A CNN model for spectrogram-based image classification was designed to analyze time and frequency domain data for gender and speaker detection.
- Classical machine learning algorithms and CNN with time and frequency domain features achieved high accuracy in gender detection using accelerometer data.
- Several studies have explored the potential for eavesdropping and spying using various sensors and signals.
Summaries
33 word summary
EarSpy captures smartphone ear speaker vibrations to spy on caller speech and identity. Motion sensors detect speech in loudspeaker reverberation. Phone manufacturers use larger speakers at the top of smartphones instead of ear.
44 word summary
EarSpy is a system that spies on caller speech and identity by capturing vibrations of smartphone ear speakers. The researchers found that motion sensors can detect speech injected into loudspeaker reverberation. Phone manufacturers use larger speakers at the top of smartphones instead of ear
518 word summary
EarSpy is a system that can spy on caller speech and identity by capturing tiny vibrations of smartphone ear speakers. The researchers found that motion sensors can detect speech injected into loudspeaker reverberation, but it was believed that eavesdropping on ear
Phone manufacturers use a larger speaker at the top of smartphones instead of ear speakers, but the volume is controlled to prevent discomfort during phone conversations. Researchers conducted an experiment using public speech datasets and found that speech information could be detected from accelerometer data. Advers
Recent studies have shown that the vibrations caused by sound transmitted through a smartphone's body can be captured by the motion sensor. The accelerometer on a smartphone has a strong response to sound frequencies from 100Hz to 3300Hz, generating aliased signals
This work focuses on realistic attack scenarios where low-volume sounds produced by the ear speaker of smartphones are used. The attack is resilient to environmental factors such as walls and nearby movements. The feasibility of playing voice through ear speakers is determined using third-party Android apps
The presented example in Figure 4 shows that only four word regions can be distinguished for the ear speaker, compared to all six word regions for the loudspeaker. The program can automatically detect at least 45% of word regions from the raw accelerometer data
We designed a CNN model for spectrogram-based image classification to analyze time and frequency domain data. Using our MATLAB program, we generated spectrograms for each word region and designed a CNN-based image classifier to detect gender, speaker, and speech. We extracted
We used the JL-Corpus and emo-DB datasets for gender and speaker detection. The JL-Corpus dataset has 2400 utterances with four speakers, while the emo-DB dataset has 535 utterances with ten actors. We
The document presents a study on using accelerometer data to detect gender and speaker identity. The study utilizes different methods, including classical machine learning algorithms and CNN with time and frequency domain features. The results show that the accuracy of gender detection using CNN with spectrograms
We achieved 86.07% and 78.12% accuracy for the FSDD dataset and 60.20% and 57.73% accuracy for the JL-Corpus dataset on OnePlus 7T and OnePlus 9 devices.
Gender Detection: Classical ML algorithms achieved a high accuracy of 98.6% in gender detection using the emo-DB dataset, and 79.37% accuracy using the JL-Corpus dataset. CNN analysis also showed similar accuracy. Ear speakers
This research focuses on speech recognition and eavesdropping on speech features such as the speaker's identity and gender using the ear speaker-induced vibration in an accelerometer. The study found high accuracy in gender detection (98.6%), speaker detection (92.
Several studies have explored the potential for eavesdropping and spying using various sensors and signals. One study focused on extracting and separating audio vibrations through walls using wireless signals. Another study investigated acoustic eavesdropping through wireless vibrometry. A data
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The text includes various sources and references related to eavesdro