A popular kind of image manipulation is copy-move (CM) forgery, which entails copying and pasting a section of a picture to hide or duplicate material. An essential component of digital picture forensics is the detection of such frauds. Convolutional neural networks (CNNs), one type of deep learning technique, are used to extract informative characteristics from photographs. CNNs are well-suited for image-related tasks like forgery detection because of their reputation for being able to capture intricate patterns and structures. A reptile search algorithm using a deep transfer learning-based CM forgery detection (RSADTL-CMFD) technique is presented in this research. Neural Architectural Search Network (NASNet) feature extraction in forgery detection is used in the model that is being presented. This enables the network to efficiently extract discriminative and pertinent features from the input photos. To improve we use the reptile search algorithm (RSA) for hyperparameter tuning in order to optimize the NASNet model's performance. By optimizing the network's hyperparameters, this approach helps the model perform better and quickly adjust to various forgery detection tasks. Lastly, extreme gradient boosting (XGBoost) efficiently classifies areas of the image as authentic or manipulated/forged by using the features that were retrieved from the deep learning network. Benchmark datasets are used to test the RSADTL-CMFD model's experimental result analysis. A thorough comparison study demonstrated how the RSADTL-CMFD approach produced better results than more contemporary approaches.