An alternative method for Image Encryption by using Neural Networks
Satyanarayana Vollala · 2024
Information security has emerged as a requirement and a hot topic in the modern digital world. The world today is surrounded by tonnes of gigabytes of data generated from digital platforms continually streaming through the Internet. In this perspective, computer scientists are strongly pushed to protect information against unscrupulous persons who evolve quickly with technology. The goal of cryptography is to encrypt and decrypt information by using complex mathematical functions and logics. Encryption not only for text data, but image, audio, and video kind of data as well. This research focuses on encryption and decryption of images. Different kinds of image encryption algorithms have been developed upto this point. In each algorithm, pixel permutation and pixel substitution steps are performed by using mathematical functions such as Logistic map, Cubic-logistic map, and Arnold map. Various Image cryptanalysis works has been going on these conventional image encryption algorithms. In this work, we introduce a new method of encrypting and decrypting images without using any conventional steps like substitution and permutation of image pixels. This proposed method uses a deep learning technique, i.e., convolutional autoencoders. This method targets both compression and encryption so that images can travel with much speed and security over the network.