Signal multiple encodings by using autoencoder deep learning

Ammar Sameer Anaz, Moatasem Yaseen Al-Ridha, Raid Rafi Omar Al-Nima


Encryption is a substantial phase in information security. It permits only approved persons to get private information. This study suggests a signal multi-encryptions system (SMES) technique for coding and decoding signals created by a deep autoencoder network (DAN). The DAN of four layers is employed for a coding package of signals multiple times before decoding or restructuring the original signals again. The suggested SMES offers a high level of security as it can produce and exploit multiple encryptions for signals. Many statistical calculations are applied to measure the reliability of the system. The outcomes are promising where noteworthy encryptions-decryptions are obtained.


Autoencoder deep learning; Decryption; Encryption; Security

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