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Deep Learning-based Denoising of TEMPEST Images for Efficient Optical Character Recognition.pdf (888.23 kB)
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Deep Learning-based Denoising of TEMPEST Images for Efficient Optical Character Recognition

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posted on 22.06.2020 by Juan Galvis, Chaouki Kasmi, Felix Vega, Santiago Morales Aguilar
The present work shows the application of deep learning models to the denoising of video frames retrieved from electromagnetic emanations from remote video interfaces. It has been demonstrated that the cables of video interfaces like VGA or HDMI, produce unintended emanations, and that these emanations can be received and processed to reconstruct the video frames displayed on the external monitor. However, the reconstructed frames are noisy, making it difficult to recover any useful information. By applying deep learning models to denoise, deblur, and interpret the images, information can be interpreted.

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Email Address of Submitting Author

juan.galvis@derc.tii.ae

Submitting Author's Institution

Directed Energy Research Centre, Technology Innovation Institute

Submitting Author's Country

United Arab Emirates

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