The recovery scheme of computer-generated holography encryption–hiding images based on deep learning
In order to improve the security of ciphertext images in transmission, a scheme of using neural network is proposed to restore the encryption–hiding images based on chaotic iris phase mask and computer-generated holography (CGH), which solve the great difficulty of illegal attacks in the symmetric–a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hu, Tao [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Effect of hawthorn seed extract on the gastrointestinal function of rats with diabetic gastroparesis - Niu, Zhenzhen ELSEVIER, 2020, Amsterdam |
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| Übergeordnetes Werk: |
volume:529 ; year:2023 ; day:15 ; month:02 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.optcom.2022.129100 |
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| 245 | 1 | 4 | |a The recovery scheme of computer-generated holography encryption–hiding images based on deep learning |
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| 520 | |a In order to improve the security of ciphertext images in transmission, a scheme of using neural network is proposed to restore the encryption–hiding images based on chaotic iris phase mask and computer-generated holography (CGH), which solve the great difficulty of illegal attacks in the symmetric–asymmetric hybrid encryption system. Firstly, the ciphertext image based on CGH and chaotic iris phase mask is generated, and the ciphertext image is hidden into a carrier image. A large number of hidden image and plaintext image pairs are produced as a dataset. Next, the neural network is built by continuous training and testing. Finally, the established neural network can fit the mapping relationship between the hidden image and the plaintext image. It is not necessary to extract the ciphertext image from the hidden image before decrypting. We compare the image recovered by the neural network with the plaintext image. The experimental results show that the average cross-correlation coefficient (CC) is 0.994, the average peak signal-to-noise ratio (PSNR) is 70.2 dB, and the average structural similarity (SSIM) is 0.929. This scheme can directly realize the decryption of the hidden image. The scheme (encryption–decryption–hiding) are elaborated in detail. The simulation experiments show that the scheme is feasible and has good robustness. | ||
| 520 | |a In order to improve the security of ciphertext images in transmission, a scheme of using neural network is proposed to restore the encryption–hiding images based on chaotic iris phase mask and computer-generated holography (CGH), which solve the great difficulty of illegal attacks in the symmetric–asymmetric hybrid encryption system. Firstly, the ciphertext image based on CGH and chaotic iris phase mask is generated, and the ciphertext image is hidden into a carrier image. A large number of hidden image and plaintext image pairs are produced as a dataset. Next, the neural network is built by continuous training and testing. Finally, the established neural network can fit the mapping relationship between the hidden image and the plaintext image. It is not necessary to extract the ciphertext image from the hidden image before decrypting. We compare the image recovered by the neural network with the plaintext image. The experimental results show that the average cross-correlation coefficient (CC) is 0.994, the average peak signal-to-noise ratio (PSNR) is 70.2 dB, and the average structural similarity (SSIM) is 0.929. This scheme can directly realize the decryption of the hidden image. The scheme (encryption–decryption–hiding) are elaborated in detail. The simulation experiments show that the scheme is feasible and has good robustness. | ||
| 650 | 7 | |a Deep learning |2 Elsevier | |
| 650 | 7 | |a Image hiding |2 Elsevier | |
| 650 | 7 | |a Optical encryption |2 Elsevier | |
| 650 | 7 | |a Computer-generated holography |2 Elsevier | |
| 700 | 1 | |a Ying, Yuchen |4 oth | |
| 700 | 1 | |a Sun, Xueru |4 oth | |
| 700 | 1 | |a Jin, Weimin |4 oth | |
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10.1016/j.optcom.2022.129100 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001982.pica (DE-627)ELV059744898 (ELSEVIER)S0030-4018(22)00747-7 DE-627 ger DE-627 rakwb eng 580 VZ AFRIKA DE-30 fid BIODIV DE-30 fid 42.38 bkl Hu, Tao verfasserin aut The recovery scheme of computer-generated holography encryption–hiding images based on deep learning 2023transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In order to improve the security of ciphertext images in transmission, a scheme of using neural network is proposed to restore the encryption–hiding images based on chaotic iris phase mask and computer-generated holography (CGH), which solve the great difficulty of illegal attacks in the symmetric–asymmetric hybrid encryption system. Firstly, the ciphertext image based on CGH and chaotic iris phase mask is generated, and the ciphertext image is hidden into a carrier image. A large number of hidden image and plaintext image pairs are produced as a dataset. Next, the neural network is built by continuous training and testing. Finally, the established neural network can fit the mapping relationship between the hidden image and the plaintext image. It is not necessary to extract the ciphertext image from the hidden image before decrypting. We compare the image recovered by the neural network with the plaintext image. The experimental results show that the average cross-correlation coefficient (CC) is 0.994, the average peak signal-to-noise ratio (PSNR) is 70.2 dB, and the average structural similarity (SSIM) is 0.929. This scheme can directly realize the decryption of the hidden image. The scheme (encryption–decryption–hiding) are elaborated in detail. The simulation experiments show that the scheme is feasible and has good robustness. In order to improve the security of ciphertext images in transmission, a scheme of using neural network is proposed to restore the encryption–hiding images based on chaotic iris phase mask and computer-generated holography (CGH), which solve the great difficulty of illegal attacks in the symmetric–asymmetric hybrid encryption system. Firstly, the ciphertext image based on CGH and chaotic iris phase mask is generated, and the ciphertext image is hidden into a carrier image. A large number of hidden image and plaintext image pairs are produced as a dataset. Next, the neural network is built by continuous training and testing. Finally, the established neural network can fit the mapping relationship between the hidden image and the plaintext image. It is not necessary to extract the ciphertext image from the hidden image before decrypting. We compare the image recovered by the neural network with the plaintext image. The experimental results show that the average cross-correlation coefficient (CC) is 0.994, the average peak signal-to-noise ratio (PSNR) is 70.2 dB, and the average structural similarity (SSIM) is 0.929. This scheme can directly realize the decryption of the hidden image. The scheme (encryption–decryption–hiding) are elaborated in detail. The simulation experiments show that the scheme is feasible and has good robustness. Deep learning Elsevier Image hiding Elsevier Optical encryption Elsevier Computer-generated holography Elsevier Ying, Yuchen oth Sun, Xueru oth Jin, Weimin oth Enthalten in Niu, Zhenzhen ELSEVIER Effect of hawthorn seed extract on the gastrointestinal function of rats with diabetic gastroparesis 2020 Amsterdam (DE-627)ELV004103645 volume:529 year:2023 day:15 month:02 pages:0 https://doi.org/10.1016/j.optcom.2022.129100 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-AFRIKA FID-BIODIV 42.38 Botanik: Allgemeines VZ AR 529 2023 15 0215 0 |
| spelling |
10.1016/j.optcom.2022.129100 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001982.pica (DE-627)ELV059744898 (ELSEVIER)S0030-4018(22)00747-7 DE-627 ger DE-627 rakwb eng 580 VZ AFRIKA DE-30 fid BIODIV DE-30 fid 42.38 bkl Hu, Tao verfasserin aut The recovery scheme of computer-generated holography encryption–hiding images based on deep learning 2023transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In order to improve the security of ciphertext images in transmission, a scheme of using neural network is proposed to restore the encryption–hiding images based on chaotic iris phase mask and computer-generated holography (CGH), which solve the great difficulty of illegal attacks in the symmetric–asymmetric hybrid encryption system. Firstly, the ciphertext image based on CGH and chaotic iris phase mask is generated, and the ciphertext image is hidden into a carrier image. A large number of hidden image and plaintext image pairs are produced as a dataset. Next, the neural network is built by continuous training and testing. Finally, the established neural network can fit the mapping relationship between the hidden image and the plaintext image. It is not necessary to extract the ciphertext image from the hidden image before decrypting. We compare the image recovered by the neural network with the plaintext image. The experimental results show that the average cross-correlation coefficient (CC) is 0.994, the average peak signal-to-noise ratio (PSNR) is 70.2 dB, and the average structural similarity (SSIM) is 0.929. This scheme can directly realize the decryption of the hidden image. The scheme (encryption–decryption–hiding) are elaborated in detail. The simulation experiments show that the scheme is feasible and has good robustness. In order to improve the security of ciphertext images in transmission, a scheme of using neural network is proposed to restore the encryption–hiding images based on chaotic iris phase mask and computer-generated holography (CGH), which solve the great difficulty of illegal attacks in the symmetric–asymmetric hybrid encryption system. Firstly, the ciphertext image based on CGH and chaotic iris phase mask is generated, and the ciphertext image is hidden into a carrier image. A large number of hidden image and plaintext image pairs are produced as a dataset. Next, the neural network is built by continuous training and testing. Finally, the established neural network can fit the mapping relationship between the hidden image and the plaintext image. It is not necessary to extract the ciphertext image from the hidden image before decrypting. We compare the image recovered by the neural network with the plaintext image. The experimental results show that the average cross-correlation coefficient (CC) is 0.994, the average peak signal-to-noise ratio (PSNR) is 70.2 dB, and the average structural similarity (SSIM) is 0.929. This scheme can directly realize the decryption of the hidden image. The scheme (encryption–decryption–hiding) are elaborated in detail. The simulation experiments show that the scheme is feasible and has good robustness. Deep learning Elsevier Image hiding Elsevier Optical encryption Elsevier Computer-generated holography Elsevier Ying, Yuchen oth Sun, Xueru oth Jin, Weimin oth Enthalten in Niu, Zhenzhen ELSEVIER Effect of hawthorn seed extract on the gastrointestinal function of rats with diabetic gastroparesis 2020 Amsterdam (DE-627)ELV004103645 volume:529 year:2023 day:15 month:02 pages:0 https://doi.org/10.1016/j.optcom.2022.129100 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-AFRIKA FID-BIODIV 42.38 Botanik: Allgemeines VZ AR 529 2023 15 0215 0 |
| allfields_unstemmed |
10.1016/j.optcom.2022.129100 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001982.pica (DE-627)ELV059744898 (ELSEVIER)S0030-4018(22)00747-7 DE-627 ger DE-627 rakwb eng 580 VZ AFRIKA DE-30 fid BIODIV DE-30 fid 42.38 bkl Hu, Tao verfasserin aut The recovery scheme of computer-generated holography encryption–hiding images based on deep learning 2023transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In order to improve the security of ciphertext images in transmission, a scheme of using neural network is proposed to restore the encryption–hiding images based on chaotic iris phase mask and computer-generated holography (CGH), which solve the great difficulty of illegal attacks in the symmetric–asymmetric hybrid encryption system. Firstly, the ciphertext image based on CGH and chaotic iris phase mask is generated, and the ciphertext image is hidden into a carrier image. A large number of hidden image and plaintext image pairs are produced as a dataset. Next, the neural network is built by continuous training and testing. Finally, the established neural network can fit the mapping relationship between the hidden image and the plaintext image. It is not necessary to extract the ciphertext image from the hidden image before decrypting. We compare the image recovered by the neural network with the plaintext image. The experimental results show that the average cross-correlation coefficient (CC) is 0.994, the average peak signal-to-noise ratio (PSNR) is 70.2 dB, and the average structural similarity (SSIM) is 0.929. This scheme can directly realize the decryption of the hidden image. The scheme (encryption–decryption–hiding) are elaborated in detail. The simulation experiments show that the scheme is feasible and has good robustness. In order to improve the security of ciphertext images in transmission, a scheme of using neural network is proposed to restore the encryption–hiding images based on chaotic iris phase mask and computer-generated holography (CGH), which solve the great difficulty of illegal attacks in the symmetric–asymmetric hybrid encryption system. Firstly, the ciphertext image based on CGH and chaotic iris phase mask is generated, and the ciphertext image is hidden into a carrier image. A large number of hidden image and plaintext image pairs are produced as a dataset. Next, the neural network is built by continuous training and testing. Finally, the established neural network can fit the mapping relationship between the hidden image and the plaintext image. It is not necessary to extract the ciphertext image from the hidden image before decrypting. We compare the image recovered by the neural network with the plaintext image. The experimental results show that the average cross-correlation coefficient (CC) is 0.994, the average peak signal-to-noise ratio (PSNR) is 70.2 dB, and the average structural similarity (SSIM) is 0.929. This scheme can directly realize the decryption of the hidden image. The scheme (encryption–decryption–hiding) are elaborated in detail. The simulation experiments show that the scheme is feasible and has good robustness. Deep learning Elsevier Image hiding Elsevier Optical encryption Elsevier Computer-generated holography Elsevier Ying, Yuchen oth Sun, Xueru oth Jin, Weimin oth Enthalten in Niu, Zhenzhen ELSEVIER Effect of hawthorn seed extract on the gastrointestinal function of rats with diabetic gastroparesis 2020 Amsterdam (DE-627)ELV004103645 volume:529 year:2023 day:15 month:02 pages:0 https://doi.org/10.1016/j.optcom.2022.129100 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-AFRIKA FID-BIODIV 42.38 Botanik: Allgemeines VZ AR 529 2023 15 0215 0 |
| allfieldsGer |
10.1016/j.optcom.2022.129100 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001982.pica (DE-627)ELV059744898 (ELSEVIER)S0030-4018(22)00747-7 DE-627 ger DE-627 rakwb eng 580 VZ AFRIKA DE-30 fid BIODIV DE-30 fid 42.38 bkl Hu, Tao verfasserin aut The recovery scheme of computer-generated holography encryption–hiding images based on deep learning 2023transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In order to improve the security of ciphertext images in transmission, a scheme of using neural network is proposed to restore the encryption–hiding images based on chaotic iris phase mask and computer-generated holography (CGH), which solve the great difficulty of illegal attacks in the symmetric–asymmetric hybrid encryption system. Firstly, the ciphertext image based on CGH and chaotic iris phase mask is generated, and the ciphertext image is hidden into a carrier image. A large number of hidden image and plaintext image pairs are produced as a dataset. Next, the neural network is built by continuous training and testing. Finally, the established neural network can fit the mapping relationship between the hidden image and the plaintext image. It is not necessary to extract the ciphertext image from the hidden image before decrypting. We compare the image recovered by the neural network with the plaintext image. The experimental results show that the average cross-correlation coefficient (CC) is 0.994, the average peak signal-to-noise ratio (PSNR) is 70.2 dB, and the average structural similarity (SSIM) is 0.929. This scheme can directly realize the decryption of the hidden image. The scheme (encryption–decryption–hiding) are elaborated in detail. The simulation experiments show that the scheme is feasible and has good robustness. In order to improve the security of ciphertext images in transmission, a scheme of using neural network is proposed to restore the encryption–hiding images based on chaotic iris phase mask and computer-generated holography (CGH), which solve the great difficulty of illegal attacks in the symmetric–asymmetric hybrid encryption system. Firstly, the ciphertext image based on CGH and chaotic iris phase mask is generated, and the ciphertext image is hidden into a carrier image. A large number of hidden image and plaintext image pairs are produced as a dataset. Next, the neural network is built by continuous training and testing. Finally, the established neural network can fit the mapping relationship between the hidden image and the plaintext image. It is not necessary to extract the ciphertext image from the hidden image before decrypting. We compare the image recovered by the neural network with the plaintext image. The experimental results show that the average cross-correlation coefficient (CC) is 0.994, the average peak signal-to-noise ratio (PSNR) is 70.2 dB, and the average structural similarity (SSIM) is 0.929. This scheme can directly realize the decryption of the hidden image. The scheme (encryption–decryption–hiding) are elaborated in detail. The simulation experiments show that the scheme is feasible and has good robustness. Deep learning Elsevier Image hiding Elsevier Optical encryption Elsevier Computer-generated holography Elsevier Ying, Yuchen oth Sun, Xueru oth Jin, Weimin oth Enthalten in Niu, Zhenzhen ELSEVIER Effect of hawthorn seed extract on the gastrointestinal function of rats with diabetic gastroparesis 2020 Amsterdam (DE-627)ELV004103645 volume:529 year:2023 day:15 month:02 pages:0 https://doi.org/10.1016/j.optcom.2022.129100 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-AFRIKA FID-BIODIV 42.38 Botanik: Allgemeines VZ AR 529 2023 15 0215 0 |
| allfieldsSound |
10.1016/j.optcom.2022.129100 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001982.pica (DE-627)ELV059744898 (ELSEVIER)S0030-4018(22)00747-7 DE-627 ger DE-627 rakwb eng 580 VZ AFRIKA DE-30 fid BIODIV DE-30 fid 42.38 bkl Hu, Tao verfasserin aut The recovery scheme of computer-generated holography encryption–hiding images based on deep learning 2023transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In order to improve the security of ciphertext images in transmission, a scheme of using neural network is proposed to restore the encryption–hiding images based on chaotic iris phase mask and computer-generated holography (CGH), which solve the great difficulty of illegal attacks in the symmetric–asymmetric hybrid encryption system. Firstly, the ciphertext image based on CGH and chaotic iris phase mask is generated, and the ciphertext image is hidden into a carrier image. A large number of hidden image and plaintext image pairs are produced as a dataset. Next, the neural network is built by continuous training and testing. Finally, the established neural network can fit the mapping relationship between the hidden image and the plaintext image. It is not necessary to extract the ciphertext image from the hidden image before decrypting. We compare the image recovered by the neural network with the plaintext image. The experimental results show that the average cross-correlation coefficient (CC) is 0.994, the average peak signal-to-noise ratio (PSNR) is 70.2 dB, and the average structural similarity (SSIM) is 0.929. This scheme can directly realize the decryption of the hidden image. The scheme (encryption–decryption–hiding) are elaborated in detail. The simulation experiments show that the scheme is feasible and has good robustness. In order to improve the security of ciphertext images in transmission, a scheme of using neural network is proposed to restore the encryption–hiding images based on chaotic iris phase mask and computer-generated holography (CGH), which solve the great difficulty of illegal attacks in the symmetric–asymmetric hybrid encryption system. Firstly, the ciphertext image based on CGH and chaotic iris phase mask is generated, and the ciphertext image is hidden into a carrier image. A large number of hidden image and plaintext image pairs are produced as a dataset. Next, the neural network is built by continuous training and testing. Finally, the established neural network can fit the mapping relationship between the hidden image and the plaintext image. It is not necessary to extract the ciphertext image from the hidden image before decrypting. We compare the image recovered by the neural network with the plaintext image. The experimental results show that the average cross-correlation coefficient (CC) is 0.994, the average peak signal-to-noise ratio (PSNR) is 70.2 dB, and the average structural similarity (SSIM) is 0.929. This scheme can directly realize the decryption of the hidden image. The scheme (encryption–decryption–hiding) are elaborated in detail. The simulation experiments show that the scheme is feasible and has good robustness. Deep learning Elsevier Image hiding Elsevier Optical encryption Elsevier Computer-generated holography Elsevier Ying, Yuchen oth Sun, Xueru oth Jin, Weimin oth Enthalten in Niu, Zhenzhen ELSEVIER Effect of hawthorn seed extract on the gastrointestinal function of rats with diabetic gastroparesis 2020 Amsterdam (DE-627)ELV004103645 volume:529 year:2023 day:15 month:02 pages:0 https://doi.org/10.1016/j.optcom.2022.129100 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-AFRIKA FID-BIODIV 42.38 Botanik: Allgemeines VZ AR 529 2023 15 0215 0 |
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Firstly, the ciphertext image based on CGH and chaotic iris phase mask is generated, and the ciphertext image is hidden into a carrier image. A large number of hidden image and plaintext image pairs are produced as a dataset. Next, the neural network is built by continuous training and testing. Finally, the established neural network can fit the mapping relationship between the hidden image and the plaintext image. It is not necessary to extract the ciphertext image from the hidden image before decrypting. We compare the image recovered by the neural network with the plaintext image. The experimental results show that the average cross-correlation coefficient (CC) is 0.994, the average peak signal-to-noise ratio (PSNR) is 70.2 dB, and the average structural similarity (SSIM) is 0.929. This scheme can directly realize the decryption of the hidden image. The scheme (encryption–decryption–hiding) are elaborated in detail. 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Effect of hawthorn seed extract on the gastrointestinal function of rats with diabetic gastroparesis |
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The recovery scheme of computer-generated holography encryption–hiding images based on deep learning |
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In order to improve the security of ciphertext images in transmission, a scheme of using neural network is proposed to restore the encryption–hiding images based on chaotic iris phase mask and computer-generated holography (CGH), which solve the great difficulty of illegal attacks in the symmetric–asymmetric hybrid encryption system. Firstly, the ciphertext image based on CGH and chaotic iris phase mask is generated, and the ciphertext image is hidden into a carrier image. A large number of hidden image and plaintext image pairs are produced as a dataset. Next, the neural network is built by continuous training and testing. Finally, the established neural network can fit the mapping relationship between the hidden image and the plaintext image. It is not necessary to extract the ciphertext image from the hidden image before decrypting. We compare the image recovered by the neural network with the plaintext image. The experimental results show that the average cross-correlation coefficient (CC) is 0.994, the average peak signal-to-noise ratio (PSNR) is 70.2 dB, and the average structural similarity (SSIM) is 0.929. This scheme can directly realize the decryption of the hidden image. The scheme (encryption–decryption–hiding) are elaborated in detail. The simulation experiments show that the scheme is feasible and has good robustness. |
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In order to improve the security of ciphertext images in transmission, a scheme of using neural network is proposed to restore the encryption–hiding images based on chaotic iris phase mask and computer-generated holography (CGH), which solve the great difficulty of illegal attacks in the symmetric–asymmetric hybrid encryption system. Firstly, the ciphertext image based on CGH and chaotic iris phase mask is generated, and the ciphertext image is hidden into a carrier image. A large number of hidden image and plaintext image pairs are produced as a dataset. Next, the neural network is built by continuous training and testing. Finally, the established neural network can fit the mapping relationship between the hidden image and the plaintext image. It is not necessary to extract the ciphertext image from the hidden image before decrypting. We compare the image recovered by the neural network with the plaintext image. The experimental results show that the average cross-correlation coefficient (CC) is 0.994, the average peak signal-to-noise ratio (PSNR) is 70.2 dB, and the average structural similarity (SSIM) is 0.929. This scheme can directly realize the decryption of the hidden image. The scheme (encryption–decryption–hiding) are elaborated in detail. The simulation experiments show that the scheme is feasible and has good robustness. |
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In order to improve the security of ciphertext images in transmission, a scheme of using neural network is proposed to restore the encryption–hiding images based on chaotic iris phase mask and computer-generated holography (CGH), which solve the great difficulty of illegal attacks in the symmetric–asymmetric hybrid encryption system. Firstly, the ciphertext image based on CGH and chaotic iris phase mask is generated, and the ciphertext image is hidden into a carrier image. A large number of hidden image and plaintext image pairs are produced as a dataset. Next, the neural network is built by continuous training and testing. Finally, the established neural network can fit the mapping relationship between the hidden image and the plaintext image. It is not necessary to extract the ciphertext image from the hidden image before decrypting. We compare the image recovered by the neural network with the plaintext image. The experimental results show that the average cross-correlation coefficient (CC) is 0.994, the average peak signal-to-noise ratio (PSNR) is 70.2 dB, and the average structural similarity (SSIM) is 0.929. This scheme can directly realize the decryption of the hidden image. The scheme (encryption–decryption–hiding) are elaborated in detail. The simulation experiments show that the scheme is feasible and has good robustness. |
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