Research on Key Technologies of Image Steganography Based on Simultaneous Deception of Vision and Deep Learning Models
As machine learning continues to evolve, traditional image steganography techniques find themselves increasingly unable to meet the dual challenge of deceiving both the human visual system and machine learning models. In response, this paper introduces the Visually Robust Image Steganography (VRIS)...
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MDPI AG
2024-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/22/10458 |
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| author | Fan Zhang Yanhua Dong Hongyu Sun |
| author_facet | Fan Zhang Yanhua Dong Hongyu Sun |
| author_sort | Fan Zhang |
| collection | DOAJ |
| description | As machine learning continues to evolve, traditional image steganography techniques find themselves increasingly unable to meet the dual challenge of deceiving both the human visual system and machine learning models. In response, this paper introduces the Visually Robust Image Steganography (VRIS) model, specifically tailored for this dual deception task. The VRIS model elevates visual security through the use of specialized feature extraction and processing methodologies. It meticulously conducts feature-level fusion between secret images and cover images, ensuring a high level of visual similarity between them. To effectively mislead machine learning models, the VRIS model incorporates a sophisticated strategy utilizing random noise factors and discriminators. This involves adding controlled amounts of random Gaussian noise to the encrypted image, thereby enhancing the difficulty for machine learning frameworks to recognize it. Furthermore, the discriminator is trained to discern between the noise-infused encrypted image and the original cover image. Through adversarial training, the discriminator and VRIS model refine each other, successfully deceiving the machine learning systems. Additionally, the VRIS model presents an innovative method for extracting and reconstructing secret images. This approach safeguards secret information from unauthorized access while enabling legitimate users to non-destructively extract and reconstruct images by leveraging multi-scale features from the encrypted image, combined with advanced feature fusion and reconstruction techniques. Experimental results validate the effectiveness of the VRIS model, achieving high PSNR and SSIM scores on the LFW dataset and demonstrating significant deception capabilities against the ResNet50 model on the Mini-ImageNet dataset, with an impressive misclassification rate of 99.24%. |
| format | Article |
| id | doaj-art-ef2a13faef1d49ad96115173cd7671ba |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-ef2a13faef1d49ad96115173cd7671ba2025-08-20T02:26:45ZengMDPI AGApplied Sciences2076-34172024-11-0114221045810.3390/app142210458Research on Key Technologies of Image Steganography Based on Simultaneous Deception of Vision and Deep Learning ModelsFan Zhang0Yanhua Dong1Hongyu Sun2College of Mathematics and Computer, Jilin Normal University, Siping 136000, ChinaCollege of Mathematics and Computer, Jilin Normal University, Siping 136000, ChinaCollege of Mathematics and Computer, Jilin Normal University, Siping 136000, ChinaAs machine learning continues to evolve, traditional image steganography techniques find themselves increasingly unable to meet the dual challenge of deceiving both the human visual system and machine learning models. In response, this paper introduces the Visually Robust Image Steganography (VRIS) model, specifically tailored for this dual deception task. The VRIS model elevates visual security through the use of specialized feature extraction and processing methodologies. It meticulously conducts feature-level fusion between secret images and cover images, ensuring a high level of visual similarity between them. To effectively mislead machine learning models, the VRIS model incorporates a sophisticated strategy utilizing random noise factors and discriminators. This involves adding controlled amounts of random Gaussian noise to the encrypted image, thereby enhancing the difficulty for machine learning frameworks to recognize it. Furthermore, the discriminator is trained to discern between the noise-infused encrypted image and the original cover image. Through adversarial training, the discriminator and VRIS model refine each other, successfully deceiving the machine learning systems. Additionally, the VRIS model presents an innovative method for extracting and reconstructing secret images. This approach safeguards secret information from unauthorized access while enabling legitimate users to non-destructively extract and reconstruct images by leveraging multi-scale features from the encrypted image, combined with advanced feature fusion and reconstruction techniques. Experimental results validate the effectiveness of the VRIS model, achieving high PSNR and SSIM scores on the LFW dataset and demonstrating significant deception capabilities against the ResNet50 model on the Mini-ImageNet dataset, with an impressive misclassification rate of 99.24%.https://www.mdpi.com/2076-3417/14/22/10458image steganographydual deceptionfeature-level fusionrandom noise factoradversarial training |
| spellingShingle | Fan Zhang Yanhua Dong Hongyu Sun Research on Key Technologies of Image Steganography Based on Simultaneous Deception of Vision and Deep Learning Models Applied Sciences image steganography dual deception feature-level fusion random noise factor adversarial training |
| title | Research on Key Technologies of Image Steganography Based on Simultaneous Deception of Vision and Deep Learning Models |
| title_full | Research on Key Technologies of Image Steganography Based on Simultaneous Deception of Vision and Deep Learning Models |
| title_fullStr | Research on Key Technologies of Image Steganography Based on Simultaneous Deception of Vision and Deep Learning Models |
| title_full_unstemmed | Research on Key Technologies of Image Steganography Based on Simultaneous Deception of Vision and Deep Learning Models |
| title_short | Research on Key Technologies of Image Steganography Based on Simultaneous Deception of Vision and Deep Learning Models |
| title_sort | research on key technologies of image steganography based on simultaneous deception of vision and deep learning models |
| topic | image steganography dual deception feature-level fusion random noise factor adversarial training |
| url | https://www.mdpi.com/2076-3417/14/22/10458 |
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