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Vazquez E, Torres S, Sanchez G, Avalos JG, Abarca M, Frias T, Juarez E, Trejo C, Hernandez D. Confidentiality in medical images through a genetic-based steganography algorithm in artificial intelligence. Front Robot AI 2022; 9:1031299. [PMID: 36274913 PMCID: PMC9582659 DOI: 10.3389/frobt.2022.1031299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 09/12/2022] [Indexed: 11/13/2022] Open
Abstract
Nowadays, image steganography has an important role in hiding information in advanced applications, such as medical image communication, confidential communication and secret data storing, protection of data alteration, access control system for digital content distribution and media database systems. In these applications, one of the most important aspects is to hide information in a cover image whithout suffering any alteration. Currently, all existing approaches used to hide a secret message in a cover image produce some level of distortion in this image. Although these levels of distortion present acceptable PSNR values, this causes minimal visual degradation that can be detected by steganalysis techniques. In this work, we propose a steganographic method based on a genetic algorithm to improve the PSNR level reduction. To achieve this aim, the proposed algorithm requires a private key composed of two values. The first value serves as a seed to generate the random values required on the genetic algorithm, and the second value represents the sequence of bit locations of the secret medical image within the cover image. At least the seed must be shared by a secure communication channel. The results demonstrate that the proposed method exhibits higher capacity in terms of PNSR level compared with existing works.
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Affiliation(s)
- Eduardo Vazquez
- Instituto Politécnico Nacional ESIME Culhuacan, Coyoacan, Mexico
- *Correspondence: Eduardo Vazquez, ; Giovanny Sanchez,
| | - Stephanie Torres
- Instituto Politécnico Nacional ESIME Culhuacan, Coyoacan, Mexico
| | - Giovanny Sanchez
- Instituto Politécnico Nacional ESIME Culhuacan, Coyoacan, Mexico
- *Correspondence: Eduardo Vazquez, ; Giovanny Sanchez,
| | | | - Marco Abarca
- Instituto Politécnico Nacional ESIME Culhuacan, Coyoacan, Mexico
| | - Thania Frias
- Instituto Politécnico Nacional ESIME Culhuacan, Coyoacan, Mexico
| | - Emmanuel Juarez
- Tecnológico Nacional de México, Tecnológico de Estudios Superiores de Ecatepec, Estado de México, Mexico
| | - Carlos Trejo
- Tecnológico Nacional de México, Tecnológico de Estudios Superiores de Ecatepec, Estado de México, Mexico
| | - Derlis Hernandez
- Tecnológico Nacional de México, Tecnológico de Estudios Superiores de Ecatepec, Estado de México, Mexico
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Parkinson’s disease diagnosis using neural networks: Survey and comprehensive evaluation. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2022.102909] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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A hybrid deep transfer learning-based approach for Parkinson's disease classification in surface electromyography signals. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103161] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Zhao J, Li K, Xi X, Wang S, Saravanan V, Samuel RDJ. Analysis of complex cognitive task and pattern recognition using distributed patterns of EEG signals with cognitive functions. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05439-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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