{"title":"基于量子生成对抗网络的信息隐藏方案","authors":"Jia Luo, Rigui Zhou, Yaochong Li, Guangzhong Liu","doi":"10.3724/sp.j.1089.2021.18617","DOIUrl":null,"url":null,"abstract":": Due to the insecurity of quantum image information hiding technology in the face of statisti-cal-based steganalysis algorithm detection, an information hiding scheme based on quantum generative adversarial network (QGAN) is proposed. This scheme first uses the mapping rules to map the secret information into the single qubit gate to prepare for the input state of the parameterized quantum circuit of the gen-erator G . Then the stego quantum image is generated by the generating circuit in QGAN. Finally, the sample data obtained by measuring the stego image and the real data are used as the input of the discriminator D . The iterative optimization is performed so that G can obtain a stego image close to the target image. The ex-perimental results show that proposed scheme can generate stego images that fit the target image distribution well and achieve the non-embedded hiding of information.","PeriodicalId":52442,"journal":{"name":"计算机辅助设计与图形学学报","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Information Hiding Scheme Based on Quantum Generative Adversarial Network\",\"authors\":\"Jia Luo, Rigui Zhou, Yaochong Li, Guangzhong Liu\",\"doi\":\"10.3724/sp.j.1089.2021.18617\",\"DOIUrl\":null,\"url\":null,\"abstract\":\": Due to the insecurity of quantum image information hiding technology in the face of statisti-cal-based steganalysis algorithm detection, an information hiding scheme based on quantum generative adversarial network (QGAN) is proposed. This scheme first uses the mapping rules to map the secret information into the single qubit gate to prepare for the input state of the parameterized quantum circuit of the gen-erator G . Then the stego quantum image is generated by the generating circuit in QGAN. Finally, the sample data obtained by measuring the stego image and the real data are used as the input of the discriminator D . The iterative optimization is performed so that G can obtain a stego image close to the target image. The ex-perimental results show that proposed scheme can generate stego images that fit the target image distribution well and achieve the non-embedded hiding of information.\",\"PeriodicalId\":52442,\"journal\":{\"name\":\"计算机辅助设计与图形学学报\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"计算机辅助设计与图形学学报\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.3724/sp.j.1089.2021.18617\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"计算机辅助设计与图形学学报","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.3724/sp.j.1089.2021.18617","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Computer Science","Score":null,"Total":0}
Information Hiding Scheme Based on Quantum Generative Adversarial Network
: Due to the insecurity of quantum image information hiding technology in the face of statisti-cal-based steganalysis algorithm detection, an information hiding scheme based on quantum generative adversarial network (QGAN) is proposed. This scheme first uses the mapping rules to map the secret information into the single qubit gate to prepare for the input state of the parameterized quantum circuit of the gen-erator G . Then the stego quantum image is generated by the generating circuit in QGAN. Finally, the sample data obtained by measuring the stego image and the real data are used as the input of the discriminator D . The iterative optimization is performed so that G can obtain a stego image close to the target image. The ex-perimental results show that proposed scheme can generate stego images that fit the target image distribution well and achieve the non-embedded hiding of information.