VI-NET: A hybrid deep convolutional neural network using VGG and inception V3 model for copy-move forgery classification

Abstract

Nowadays, various image editing tools are available that can be utilized for manipulating the original images; here copy-move forgery is most common forgery. In copy-move forgery, some part of the original image is copied and pasted into the same image at some other location. However, Artificial Intelligence (AI) based approaches can extract manipulated features easily. In this study, a deep learning-based method is proposed to classify the copy-move forged images. For classifying the forged images, a deep learning (DL) based hybrid model is presented named as VI-NET using fusion of two DL architectures, i.e., VGG16 and Inception V3. Further, output of two models is concatenated and connected with two additional convolutional layers. Cross-validation protocols, K10 (90 % training, 10 % testing), K5 (80 % training, 20 % testing), and K2 (50 % training, 50 % testing) are applied on the COMOFOD dataset. Moreover, the performance of VI-NET is compared with transfer learning and machine learning models using evaluation metrics such as accuracy, precision, recall, F1 score, etc. Proposed hybrid model performed better than other approaches with classification accuracy of 99 ± 0.2 % in comparison to accuracy of 95 ± 4 % (Inception V3), 93 ± 5 % (MobileNet), 59 ± 8 % (VGG16), 60 ± 1 % (Decision tree), 87 ± 1 % (KNN), 54 ± 1 % (Naïve Bayes) and 65 ± 1 % (random forest) under K10 protocol. Similarly, results are evaluated based on K2 and K5 validation protocols. It is experimentally observed that the proposed model performance is better than existing standard and customized deep learning architectures. © 2022 Elsevier Inc.