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- W3199357107 abstract "Encrypted communication using artificial intelligence is a new challenging research direction. Google Brain first proposed the generation of encrypted communication based on Generative Adversarial Networks, resulting in related discussions and research. In the encrypted communication model, when part of the plaintext is leaked to the attacker, it will cause slow decryption or even being unable to decrypt the communication party, and the high success rate of the attacker decryption, making the encrypted communication no longer secure. In the case of 16-bit plaintext symmetrical encrypted communication, the neural network model used in the original encrypted communication was optimized. The optimized communication party can complete the decryption within 1000 steps, and the error rate of the attacker is increased to more than 0.9 without affecting the decryption of the communicator, which reduces the loss rate of the entire communication to less than 0.05. The optimized neural network can ensure secure encrypted communication of information." @default.
- W3199357107 created "2021-09-27" @default.
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- W3199357107 date "2021-01-01" @default.
- W3199357107 modified "2023-09-25" @default.
- W3199357107 title "Research on Plaintext Resilient Encrypted Communication Based on Generative Adversarial Network" @default.
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- W3199357107 doi "https://doi.org/10.1007/978-981-16-5943-0_13" @default.
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