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Title |
Image Encryption and Compression Based on Auto Encoder for Real Time Using IoT Technique |
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Author |
Abdelmoty M. Ahmed, Belgacem Bouallegue, Hassan A. Youness, and Hammam M. Abdelaal |
| Citation |
Vol. 25 No. 5 pp. 63-68 |
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Abstract |
Machine Learning has completely transformed health care system, which transmits medical data through IoT sensors. So it is very important to encrypt them to protect patient data. Encrypting medical images from a performance perspective consumes time; hence the use of an auto encoder is essential. An auto encoder is used in this work to compress the image as a vector prior to the encryption process. The digital image passes across decryption function and a decoder to get back the image. In the proposed work, various experiments are carried out on hyper parameters to achieve the highest outcome of the classification. The findings demonstrate that the combination of Mean Square Logarithmic Error as the loss function, ADAgrad as an optimizer, two layers for the encoder, and another reverse for the decoder, RELU as the activation function generates the best auto encoder results. The combination of Mean square error (lose function), RMSprop (optimizer), three layers for the encoder and another reverse for the decoder, and rely (activation function) has the best classification result. All the experiments with different hyper parameter have run almost very close to each other even when changing the number of layers. The running time is between 9 and 16 second for each epoch. |
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Keywords |
Auto encoder, IoT, Image encryption, Artificial Neural Network |
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