# Cryptographic Encryption and Optimization for Internet of Things Based Medical Image Security
## 2 Literature Survey  
Haralick features are extracted, which are also known as local binary pattern (LBP), the histogram of oriented gradients (HOG), and the gray-level co-occurrence matrix (GLCM). If more features are used, the classifier will become more sophisticated and produce better results. This research employs the pigeon-inspired optimization (PIO) feature selection technique as a result. When the user submits a collection of query photos during the testing phase, an artificial neural network (ANN) classifier is used to compare the training images from the dataset to the query images in order to extract the relevant images [17]. A hyperchaotic image encryption system is based on cellular automata (CS) and the particle swarm optimization (PSO) algorithm. The initial setup of the hyperchaotic system is formed by the hash function value, which is closely related to the plaintext picture to be encrypted in order to increase resistance to plaintext attacks. The correlation coefficient between adjacent image pixels indicates PSO's fitness. Cellular automata technology, which is based on a hyper-chaotic system, can make populations more complex, diverse, and random, making encryption systems safer and preventing them from reaching a local optimum [18]. Image steganography uses a variety of techniques to provide security for the secret data, such as an image encryption method based on binary bit-plane decomposition (BBPD). After that, an adaptive embedding method based on the salp swarm optimization algorithm (SSOA) is suggested in order to maximize the payload capacity by altering the settings of the steganographic embedding function for edge and smooth blocks. In this case, the SSOA method is effectively employed to localize the edge and smooth blocks. After that, a hybrid fuzzy neural network and a backpropagation learning strategy are utilized to enhance the quality of the stego images. Finally, using an extremely secure IoT protocol, these stego images are sent to the target [19]. Interfaces that are user-friendly, enduring, secure, and scalable are necessary for the difficult challenges that arise during photo analysis and restoration. Even though there are many benefits to the cloud paradigm that have been suggested, there are still many issues with privacy and secrecy. The approach's originality comes from its use of agile, secure cloud pallets for image processing, advancement of ant colony optimization-based algorithms for better resource utilization when running compute- and resource-intensive image analysis tools [20], and extension of conventional cloud architecture. Levy flight-based gray wolf optimization is used to select the most significant features for the steganalysis method from a set of original features. For the reason, SPAM and AlexNet have been used to generate the high-dimensional features. The random forest classifier is also used to divide the photos with particular attributes into cover images and stego images [21]. A novel bilateral-based, bio-inspired optimization filtering method is taken into consideration for the MI denoising procedure (BF). Gaussian and spatial weights are affected by the implementation of the denoising procedure, or the choice of the best parameters. The swarm-based dragonfly (DF) and modified firefly (MFF) algorithms are utilized in this instance to select these options. For recreation investigation, multiobjective wellness (PSNR and VRMSE) is utilized. The denoised cycle has been used to investigate the aftereffects of the proposed BF boundaries with connection to clinical pictures once the ideal boundaries had been picked [22]. A feature extraction method for encrypted image retrieval is presented. First, we extract the image features using the enhanced Harris method. Second, the SURF technique and BOW model are integrated to build the feature vectors of each image, making it easier to obtain data from the cloud server.