FNOC: A Superficial Learning Methodology to Predict Digital Image Forgeries Using Fuzzy Enabled Neural Optimization Classifier

There has been an explosion in the production and dissemination of altered and false images in recent years, thanks to the widespread availability and user-friendliness of image editing programs. Many methods have been suggested for determining whether a image is genuine and, in certain instances, for identifying where the image has been changed or falsified. In order to assess the efficacy of the suggested system, this study introduces a novel deep learning model named Fuzzy enabled Neural Optimization Classifier (FNOC) and uses it in conjunction with the more traditional learning technique known as Support Vector Machine (SVM). In order to identify fakes, the suggested method employs a FNOC to glean characteristics based on residual noise from images. In order to differentiate between genuine and altered images, this method entails discovering the pattern of noise that the counterfeit process leaves behind. The capacity to handle undetected forgeries is a major advantage of employing a FNOC for image forgery detection. Traditional methods of detecting image forgeries can be challenged by more advanced technology. Yet, FNOCs can pick up on patterns that aren't established explicitly, so they can spot forgeries that weren't there before. The use of FNOCs to the identification of image forgeries has enormous promise in the fight against the issue of image manipulation. Digital images used in contexts ranging from medical reports to crime scene investigations might benefit from this technology's increased dependability and trustworthiness with more development.