{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:44:49Z","timestamp":1742913889614,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":7,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789813349216"},{"type":"electronic","value":"9789813349223"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,1,19]],"date-time":"2021-01-19T00:00:00Z","timestamp":1611014400000},"content-version":"vor","delay-in-days":384,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In this paper, an adversarial encryption algorithm based on generating chaotic sequence by GAN is proposed. Starting from the poor leakage resistance of the basic adversarial encryption communication model based on GAN, the network structure was improved. Secondly, this paper used the generated adversarial network to generate chaotic-like sequences as the key K and entered the improved adversarial encryption model. The addition of the chaotic model further improved the security of the key. In the subsequent training process, the encryption and decryption party and the attacker confront each other and optimize, and then obtain a more secure encryption model. Finally, this paper analyzes the security of the proposed encryption scheme through the key and overall model security. After subsequent experimental tests, this encryption method can eliminate the chaotic periodicity to a certain extent and the model\u2019s anti-attack ability has also been greatly improved. After leaking part of the key to the attacker, the secure communication can still be maintained.<\/jats:p>","DOI":"10.1007\/978-981-33-4922-3_4","type":"book-chapter","created":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T11:21:04Z","timestamp":1610968864000},"page":"37-49","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Based on GAN Generating Chaotic Sequence"],"prefix":"10.1007","author":[{"given":"Xuguang","family":"Chen","sequence":"first","affiliation":[]},{"given":"Hongbin","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Pujun","family":"Ji","sequence":"additional","affiliation":[]},{"given":"Haiting","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,19]]},"reference":[{"key":"4_CR1","unstructured":"Abadi, M., Andersen, D.G.: Learning to Protect Communications with Adversarial Neural Cryptography. 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Springer, Singapore (2019). https:\/\/doi.org\/10.1007\/978-981-13-0761-4_97"},{"key":"4_CR7","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1007\/978-981-15-0339-9_20","volume-title":"Advances in Bioinformatics, Multimedia, and Electronics Circuits and Signals","author":"J Purswani","year":"2020","unstructured":"Purswani, J., Rajagopal, R., Khandelwal, R., Singh, A.: Chaos theory on generative adversarial networks for encryption and decryption of data. In: Jain, L.C., Virvou, M., Piuri, V., Balas, V.E. (eds.) Advances in Bioinformatics, Multimedia, and Electronics Circuits and Signals. AISC, vol. 1064, pp. 251\u2013260. 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