Digital forensics is a research area designed by retrieving historical records to verify the originality of digital images. Digital photos are rendered easily using various techniques that change the images’ meaning and authenticity. The structures and algorithms for the recognition and classification of digital images into genuine and classified images within a category are included in this article. There was a review and comparison of optimization-based methods for detecting and categorising forgeries. The work proposes the identification of forgery using copy-moving pictures to illustrate how you can apply the forgery detection classification. This paper concerned the extraction features based on “Bi-orthogonal Wavelet Transform with Single Value Decomposition” (BWT-SVD) based transform transforms. New approaches and extensions to detect moving copies with an effective classification using hybrid feature extraction are described in this paper. Both methods address the issue of classifying genuine and false images in a noisy environment, and split imaging is one of them as well.