Groupwise-Spatial Attention-Based Dense Neural Network for Image Multi-Forgery Detection
Digital images have become the primary source of information exchange on digital platforms, making digital image forgery detection important and crucial in the field of digital forensics. Among various illegal manipulation techniques, copy-move and splicing manipulation are the two most common types of forgery. Copy-move forgery involves duplicating the areas within the same image, whereas splicing forgery involves taking out areas or objects from one or more images and combining them together to produce a spliced image. Detecting various forgeries in an image has become a crucial requirement due to the presence of subtle artifacts and minimal inconsistencies during the forgery process. Hence, a multi-forgery detection framework based on Groupwise-Spatial Attention–based Dense Neural Network (GSA-DNN) is proposed to detect copy-move and splicing forgery. Error level analysis (ELA) technique is used to detect image manipulation by figuring out the compression differences between authentic and forged images, followed by a generic standardizing procedure for all images based on dimensions and pixel values so as to improve final model convergence. InceptionNet V3 is employed to extract the multiscale features through convolutional kernels, which enables the learning of global and local feature representation. The proposed GSA-DNN method obtains high detection accuracy of 99.58%, 97.77%, and 94.59% on benchmark datasets such as CASIA V2, CoMoFoD, and Multiple Image Splicing Datasets compared to existing methods such as the Siamese neural network. Received: 11 May 2026 | Revised: 28 July 2026 | Accepted: 14 August 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in CASIA V2 at https://www.mdpi.com/2076-3417/14/13/5545; in CoMoFoD at https://www.kaggle.com/datasets/tusharchauhan1898/comofod; in MISD at https://zenodo.org/records/5525829 and in CG-1050 at https://www.kaggle.com/datasets/saurabhshahane/cg1050. Author Contribution Statement S. Aruna: Conceptualization, Methodology, Formal analysis, Resources, Data curation, Writing – original draft. Surabhi Narayan: Software, Validation, Investigation, Writing – review & editing, Visualization, Supervision, Project administration.