Hybrid Model for Image Forgery Detection In Smart Healthcare

Smart Healthcare Systems (SHS) are gradually replacing traditional healthcare systems that involve human intervention. This is due to the advancement of IOT devices and also the introduction of edge computing. SHS provides security, flexibility, and efficiency. A key feature of SHS is to transport images from one place to another in real-time. Due to technological advancements, these images can tamper which is image forgery, this threats security in SHS. To tackle this problem, Image Forgery Detection using Support Vector Machines (SVM) and Extreme Learning Machines (ELM) is proposed in this model. We combine the scores of both SVM and ELM using Bayesian Sum Rule (BSR). The model is utilized to verify if the forgery is done or not. This model can be inbuilt into the SHS. Forgery Detection enhances security and reduces the threat of systems being failed, as images play a key role in the treatment procedure.