Aug 2026· International journal of pattern recognition and artificial intelligence· 0 citations
TL;DR
Findings confirm that the proposed MAFN-HFL is a scalable and efficient solution for next-generation image forgery detection, and shows strong performance on two benchmark datasets.
Abstract
The threat of digital image forgery is increasingly becoming a problem to the authenticity of the media, particularly with the introduction of sophisticated editing software and Generative Artificial Intelligence (GAI). To develop a promising forgery detection framework, this research proposes a Multi-Scale Adaptive Fusion Network with Hierarchical Feature Learning (MAFN-HFL), a new Deep Learning (DL) architecture using multi-scale adaptive feature learning and fusion. The dataset consists of images of various domains, natural scenes, portraits, documents, and medical images, and their forgeries. Preprocessing involves noise removal and performing multi-resolution decomposition. Notably, the proposed MAFNHFL incorporates a Multi-Scale Convolutional Attention Network (MSCAN) that extracts both local and global forgery artifacts, an Adaptive Feature Fusion Module (AFFM) that adaptively fuses multi-resolution features according to the manipulation context, and Hierarchical Feature Learning with skip connections to preserve fine-grained visual details. The features of the spatial and frequency domains are captured by a proposed ResNet-50 and Discrete Cosine Transform (DCT) analysis, respectively, through a dual-branch architecture. An Artifact-Aware Attention mechanism achieves a further focus on tampered regions. The hybrid CNNa-Transformer classifier using a confidence-weighted ensemble is capable of providing binary classification as well as localization of manipulated pixels. The proposed model was evaluated on two benchmark datasets using a 70:30 training-test split. Experimental results show strong performance, achieving classification accuracy as high as 99.21% with consistently high precision and low error rates across both datasets. These findings confirm that the proposed MAFN-HFL is a scalable and efficient solution for next-generation image forgery detection.
Establishing the authenticity of digital images is widely acknowledged as a crucial aspect of multimedia security since contemporary alteration techniques like copy-move, splicing, and retouching create remarkably convincing forgeries that are difficult to spot. This research proposes a Deep Learning (DL) based image forgery detection methodology that combines attention-guided segmentation and hybrid feature extraction to deliver robust tamper detection and classification. The preprocessing procedures including resizing and normalizing images are used to ensure consistent model performance. The hybrid encoder, records local texture-level inconsistencies and long-range contextual dependencies, composed of Swin Transformer modules and Residual-Dense blocks. Multi-scale feature maps that are highly discriminative for forgery cues are the outcome of this. These features are decoded using a UNet++ segmentation network that has been altered using Channel-Dual Attention (CA-DA). This improves the spatial focus and boundary precision of the projected forgery mask. The created mask is analyzed in a detection stage, where threshold-based decision-making mechanism determines if the input is genuine or forged, non-empty masks that exceed the threshold indicate tampered images, whereas actual images are represented by empty masks. The proposed system is implemented in Python software, and the results demonstrate that the proposed experimental validation on benchmark datasets indicates improved detection accuracy, robustness to noise compression, and localization clarity when compared to conventional approaches.
Sruthi Anand, V. Saranya· ITEGAM- Journal of Engineeri...· 0 citations
A fusion-based lightweight deep learning framework for copy-move image forgery detection and localization that offers an efficient and practical solution for digital image authentication and is applicable to digital forensics, journalism, law enforcement, cyber security, and multimedia content verification.
K. Sumalini, K. B. Maruthiram· International Journal of Res...· 0 citations
The evolution of sophisticated generative artificial intelligence has led to the rapid development of very realistic manipulated images and videos, posing substantial risks for digital trust, cyber security, and multimedia authenticity. Advanced Deepfake generation technologies result in the creation of believable forgery media which become hard to differentiate from authentic media; this leads to misinformation, identity spoofing, and digital scams. Therefore, precise and effective authentication of multimedia becomes an imperative requirement for digital forensics investigation and online content authentication. This research paper presents a ResNet-powered deep feature learning approach for detecting Deepfake images and videos. The suggested approach normalizes and resizes images, while videos are decomposed into frames for thorough spatial-temporal analysis. The hybrid convolutional neural network model, which is built on top of ResNet architecture, extracts discriminative features that represent subtle manipulation traces, face texture inconsistency, and structural abnormalities. In addition, inverted residual blocks and linear bottlenecks are used to increase computational efficiency. The deep learning-based feature extraction process is then followed by the classification stage to distinguish between genuine multimedia content and forged multimedia content. From experimental studies, it can be shown that the proposed framework helps to enhance the detection rate, robustness toward new Deepfake methods, and enables real-time implementation. The research provides an effective solution for multimedia authentication applications.
Bella Inba Suganthi V, S. Jose· International Journal of Sci...· 0 citations
Increasing availability of advanced AI-based image creation and editing tools has significantly increased the prevalence of digital image forgery, creating challenges for media authenticity, digital forensics, and information security. This paper presents an intelligent image forgery detection framework that combines multi-quality Error Level Analysis (ELA) with an optimized Convolutional Neural Network (CNN) and heat map-based manipulation localization. The proposed approach utilizes ELA preprocessing at multiple JPEG quality levels to amplify compression inconsistencies associated with image tampering, this is followed by a lightweight CNN architecture trained using advanced optimization techniques, including warmup and cosine annealing learning-rate scheduling, stochastic gradient descent with momentum, and class imbalance handling. To improve interpretability, a heat map generation module is introduced to localize suspicious image regions and provide visual evidence supporting classification decisions which provides interpretability and explainability. The framework was evaluated using the CASIA 2.0 image forgery dataset containing a total of 12,614 images, 7,491 authentic and 5,123 tampered images. Experimental results demonstrate an overall classification accuracy of 94%, outperforming a baseline ELA-CNN implementation while maintaining computational efficiency. The proposed localization mechanism enhances explainability by highlighting potential manipulation regions. A web-based interface and RESTful API were developed to support practical deployment and integration into digital forensic workflows. The results indicate that the proposed framework provides an effective and interpretable smart solution for automated image forgery detection and localization.
Ahmad AlMunayyer, Abdallah Banat, Mohammad AlHayajneh et al.· IEEE Jordan Conference on Ap...· 0 citations
This paper develops a dual-path generator architecture: the standard path thoroughly extracts deep high-level features, while the auxiliary path preserves prior information and high-frequency details, and introduces a multi-scale feature extraction module to boost the model’s ability to capture features at various scales.
Long-Jie Wang, Dan-Dan Huang, N. Ruan et al.· Scientific Reports· 0 citations
In the era of advanced digital image generation techniques such as copy-move forgery, the NDV is having increasing concerns regarding image authenticity particularly with the spread of digital images through social media, media and courts. This type of forgery, where a part of an image is replicated and pasted on the same image, to cover objects or materials poses severe challenges to the traditional approaches due to its inherent compactness in color, texture and noise. This research primarily focuses on the problem that deep learning models currently do not have sufficient interpretability and performance for the reliable detection and explanation of such forgeries under different image manipulations such as scaling, rotation and compression. This study proposes an explainable deep learning model which is capable of detecting copy-move forgery with high accuracy and is explainable as well from both feature and visual insights. The proposed technique has a hybrid CNN with both attention and feature matching modules, and an explainability module with Gradient-weighted Class Activation Mapping (Grad-CAM). The suggested framework has been evaluated on the benchmark datasets CoMoFoD and CASIA where accuracy, precision, recall and F1-scores were found to be 98.3, 97.6, 98.1 and 97.8 respectively which is higher than the other five state-of-the-art methods by 2-4 points. The approach was also able to withstand geometric transformations and compression and explainable by heatmap imaging. In conclusion, the proposed framework improves the detection and the interpretability of the deep learning-based copy-move forgery detection system, therefore it can be used in the domain of practical forensic research.
Shaheena K. V., D. S· International Conference on...· 0 citations
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