Modeling of Reptile Search Algorithm With Deep Learning Approach for Copy Move Image Forgery Detection

Abstract

Copy-move (CM) forgery is a common type of image manipulation that involves copying and pasting a region within an image to conceal or duplicate content. Detection of such forgeries acts as an important part of digital image forensics. Deep learning techniques, such as convolutional neural networks (CNNs), are employed to extract informative features from images. CNNs are known for their ability to capture complex patterns and structures, making them well-suited for image-related tasks like forgery detection. This paper introduces a reptile search algorithm with a deep transfer learning-based CM forgery detection (RSADTL-CMFD) approach. The presented model uses Neural Architectural Search Network (NASNet) for feature extraction in forgery detection which allows the network to effectively capture relevant and discriminative features from the input images. To enhance the performance of the NASNet model, we employ the reptile search algorithm (RSA) for hyperparameter tuning. This algorithm optimizes the network’s hyperparameters, enabling the model to quickly adapt to different forgery detection tasks and achieve superior performance. Finally, extreme gradient boosting (XGBoost) effectively utilizes the extracted features from the deep learning network to classify regions within the image as genuine or manipulated/forged. The experimental result analysis of the RSADTL-CMFD model is tested using benchmark datasets. An extensive comparative study highlighted the enhanced outcomes of the RSADTL-CMFD method over recent techniques.

Keywords

• Cybersecurity
• image forgery
• copy move detection
• machine learning
• deep learning
• parameter optimization