Secure Image Encryption using Optimum Key generation with Deep learning Technique in Cloud Storage Environment

Abstract

Many industries, including healthcare, the military, finance, and more, need extra protection for the interchange of picture data since images are now sent across open channels that might be attacked. In order to protect the system against differential and brute force assaults, the security aspects are crucial. The transmission of multimedia, including digital pictures, text, audio, and video, relies heavily on encryption to maintain secrecy, integrity, and confidentiality while preventing unwanted access to critical information. Even while chaos-based cryptosystems aren't as widely used as AES, DES, or RSA, they've been a hot topic of study recently and can enhance the security of public key cryptosystems when combined with them. With the rise of deep convolutional neural networks (CNNs) as the go-to machine learning technology for many uses, there have been several efforts to use CNNs to decipher encrypted data. On the other hand, prior research has paid little attention to protecting model parameters and has instead concentrated on protecting data. Additionally, they provide high-level implementations without thoroughly analyzing the trade-offs between speed, security, and accuracy in the ECC implementation of common CNN basic operators like non-linear activation, convolution, along with pooling.The goal of this research is to develop and construct a cryptosystem based on Chaos that can effectively encrypt images and withstand differential assaults. In order to create the first layer of encryption, the system first divides the original picture into smaller pieces and rearranges them. A logistic map is used to generate a one-dimensional sequence, which is then multiplied over the highest pixel value and processed bit by bit as part of the encryption process. We use the outcome to encrypt the picture, and then apply the same procedure to decode it. For efficient key generation during picture encryption, the suggested model uses the ECC approach to produce a Dung Beetle optimization (DBO). For improved security performance, the chaotic map notion is introduced to the robust optimization approach. The results of the investigation demonstrate that the suggested approach provides significantly improved security performance while leaving picture quality unaffected.The histogram, Pearson's correlation analysis, peak signal-to-noise ratio (PSNR), entropy, number of pixels change rate (NPCR), and unified average fluctuation in intensity (UACI) are used to assess the encryption outcomes. Our findings prove that the suggested strategy is safe, dependable, efficient, and adaptable.