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Surabhi Narayan

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Review Open access Aug 2026

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.

S. Aruna, Surabhi Narayan · Artificial Intelligence and... · 0 citations

Open access Aug 2026

Attention-Guided Cross-Connected Filters Convolutional Neural Network with Surrogate-Based Interpretability for Image Splicing Forgery Detection

Background: Image splicing forgery detection is one of the most challenging problems in the field of image forensics as it involves identifying and localizing suspicious regions that are created by integrating contents from one or more different sources. The accurate detection and classification of splicing forgery still remains a difficult task because of the existence of overlapping image regions, which makes it complex to differentiate authentic and tampered images. This overlap causes a lack of feature representation, making it difficult to precisely detect the tampering in images. Also, the decision-making process of the model is often a black box, which makes it challenging to interpret and understand the rationale behind its decisions. Methods: To address these challenges, a Convolutional Block Attention Module (CBAM)–U-Net with Cross-Connected Filters–Convolutional Neural Network (CCF-CNN) is proposed to achieve precise detection and localization of spliced regions. The CBAM enhances spatial and channel-wise attention, enabling accurate localization of forged regions. The dual-phase CCF-CNN is incorporated with cross-connected filters to differentiate between the authentic and tampered regions by extracting global and local features. Additionally, a surrogate heatmap mechanism is introduced using intermediate decoder features to generate patch-level visual explanations, enabling precise localization of the spliced regions, thereby improving the model’s transparency in decision-making. Results: The proposed CCF-CNN obtains a high accuracy of 99.84% on the CASIA 2.0 dataset and an accuracy of 95.63% on the MISD. Conclusions: Compared to traditional CNNs such as VGG, ResNet and attention-based interpretability algorithms, the proposed model obtains higher performance in terms of detection and interpretability.

Aruna Srinivasan, Surabhi Narayan, Aarnav Sandeep Deshmukh · Computers · 0 citations