Securing Multimedia using a Deep Learning based Chaotic Logistic Map

Telemedicine and online consultations with doctors has become very popular during the pandemic and involves the transmission of medical data through the internet. Thus this raises concern about the security of the medical data of the patient as the records to contain sensitive and confidential information. A Secure multimedia transformation approach is proposed in this paper using a deep learning-based chaotic logistic map. The proposed work achieves novelty by the integration of a lightweight encryption function using a chaotic logistic map. It also uses the ResNet model to perform classification for identifying the fake medical multimedia data. A linear feedback shift register operations and an interactive user interface facilitate ease of usage to secure the medical multimedia data. The chaotic map provides the security properties such as confusion and diffusion necessary for the encryption ciphers. At the same time, they are highly sensitive to input conditions, thus making the proposed encryption algorithm more secure and robust. The proposed encryption mechanism helps in securing the medical image and video data. On the receiver side, Multilayer perceptions (MLP) of the deep learning approach are used to classify the medical data according to the features required to make other processes. When tested, the proposed work proves efficient in securing medical data against various cyber-attacks and exhibits high entropy levels.