Image Forgery Detection Using Combined 2D DWT-Neural Network

Image forgery refers to the act of altering or modifying digital images in a way that deceives viewers about the authenticity of the content. Typically, the purpose of picture forgery is to manipulate or deceive the viewer by providing inaccurate or misleading information. Copy-move forgery is one of the most frequently employed forms of forgery. The most important challenge in this field is an automated forgery detector module. A proposed Two Dimensional Discrete Wavelet Transform (2D DWT) and Neural Network forgery detection module is included in the study in question. The proposed strategy makes use of 2D DWT segments at four levels and subsequently, blocks are produced from the reduced image (approximation at level four). By utilizing the Image Database for Copy-Move Forgery Detection (CoMoFoD), the presented module can identify forged images with an average accuracy that is superior to other techniques.