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README.md
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README.md
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The [**gr-tempest**](https://github.com/git-artes/gr-tempest) project (*monitor image espionage in GNU Radio*) is extended using *deep learning* to improve the quality of the spied images. The main goal is to recover the text present in the image captured by the espionage system.
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You can also find a video of the system in operation, [available at this link](https://www.youtube.com/watch?v=ig3NWg_Yzag).
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## Cite this work (TODO)
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## Data
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The data used can be found in [this link](https://www.dropbox.com/scl/fi/7r2o8nbws45q30j5lkxjb/deeptempest_dataset.zip?rlkey=w7jvw275hu8tsyflgdkql7l1c&st=e8rdldz0&dl=0) within a ZIP file (~7GB). After unzipping, you will find synthetic and captured images used for experiments, training, and evaluation during the work.
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git clone https://github.com/emidan19/deep-tempest.git
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```
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Both [gr-tempest](./gr-tempest/) and [end-to-end](./end-to-end/) folders contains a guide on how to execute the corresponding files for image capturing, inference and train the deep learning architecture based on DRUNet from [KAIR](https://github.com/cszn/KAIR/tree/master) image restoration repository.
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Both [gr-tempest](./gr-tempest/) and [end-to-end](./end-to-end/) folders contains a guide on how to execute the corresponding files for image capturing, inference and train the deep learning architecture based on DRUNet from [KAIR image restoration repository](https://github.com/cszn/KAIR/tree/master).
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The code is written in Python version 3.10, using Anaconda environments. To replicate the working environment, create a new one with the libraries listed in [*requirements.txt*](./requirements.txt):
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conda activate deeptempest
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```
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Regarding installations with GNU Radio, **it is necessary to follow the [gr-tempest](https://github.com/git-artes/gr-tempest) instructions** (detailed below) and then run the following *grc* files flowgraphs to activate the *hierblocks*:
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Regarding installations with GNU Radio, **it is necessary to follow the [gr-tempest](./gr-tempest/README.md) instructions in this repository** *(even if you have already installed gr-tempest at a different location)*. After this, run the following *grc* files flowgraphs to activate the *hierblocks*:
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- [binary_serializer.grc](./gr-tempest/examples/binary_serializer.grc)
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- [FFT_autocorrelate.grc](./gr-tempest/examples/FFT_autocorrelate.grc)
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- [FFT_crosscorrelate.grc](./gr-tempest/examples/FFT_crosscorrelate.grc)
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- [Keep_1_in_N_frames.grc](./gr-tempest/examples/Keep_1_in_N_frames.grc)
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Finally run the flowgraph [deep-tempest_example.grc](./gr-tempest/examples/deep-tempest_example.grc) to capture the monitor images and be able to recover them with better quality using the *Save Capture* block.
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## References
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```BibTex
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@INPROCEEDINGS{larroca2022gr_tempest,
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author={Larroca, Federico and Bertrand, Pablo and Carrau, Felipe and Severi, Victoria},
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booktitle={2022 Asian Hardware Oriented Security and Trust Symposium (AsianHOST)},
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title={gr-tempest: an open-source GNU Radio implementation of TEMPEST},
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year={2022},
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doi={10.1109/AsianHOST56390.2022.10022149}}
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@article{zhang2021plug,
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title={Plug-and-Play Image Restoration with Deep Denoiser Prior},
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author={Zhang, Kai and Li, Yawei and Zuo, Wangmeng and Zhang, Lei and Van Gool, Luc and Timofte, Radu},
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journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
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year={2021}
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}
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