TY - GEN
T1 - Image to Image Steganography using U-Net Architecture with MobileNet Convolutional Neural Network
AU - Jenynof, Arino
AU - Ahmad, Tohari
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Steganography conceals information inside another object, so information is not immediately apparent in the examination. Different from encryption, where a ciphertext is an encrypted object, steganography aims to hide information and the fact that a piece of information is concealed. In the case of image steganography, the secret is embedded into a cover image so that the cover image does not differ substantially, and the secret can be extracted without too many alterations. This paper proposes a method to embed an image into another image using U-Net architecture with MobileNet Convolutional Neural Network. The proposed method is trained and tested on a readily available image dataset, CIFAR10, StanfordCars, and STL10. The result shows that the proposed method can embed and extract images from CIFAR10, StanfordCars, and STL10 datasets with average PSNR of 28.165 dB, 28.539 dB, and 28.226 dB for steganographic images; 27.075 dB, 29.759 dB, and 28.767 dB for extracted images.
AB - Steganography conceals information inside another object, so information is not immediately apparent in the examination. Different from encryption, where a ciphertext is an encrypted object, steganography aims to hide information and the fact that a piece of information is concealed. In the case of image steganography, the secret is embedded into a cover image so that the cover image does not differ substantially, and the secret can be extracted without too many alterations. This paper proposes a method to embed an image into another image using U-Net architecture with MobileNet Convolutional Neural Network. The proposed method is trained and tested on a readily available image dataset, CIFAR10, StanfordCars, and STL10. The result shows that the proposed method can embed and extract images from CIFAR10, StanfordCars, and STL10 datasets with average PSNR of 28.165 dB, 28.539 dB, and 28.226 dB for steganographic images; 27.075 dB, 29.759 dB, and 28.767 dB for extracted images.
KW - Data hiding
KW - convolutional neural network
KW - image steganography
KW - information security
KW - infrastructure
UR - http://www.scopus.com/inward/record.url?scp=85179845661&partnerID=8YFLogxK
U2 - 10.1109/ICCCNT56998.2023.10306352
DO - 10.1109/ICCCNT56998.2023.10306352
M3 - Conference contribution
AN - SCOPUS:85179845661
T3 - 2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023
BT - 2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023
Y2 - 6 July 2023 through 8 July 2023
ER -