These findings show that complementary visual and metadata cues are much more useful in detection, while the use of a lightweight backbone enables efficient, high-throughput forensic analysis suitable for real-world deployment.
Abstract
Multimedia data have been continuously increasing in magnitude, and so has the sophistication of manipulation methods, thereby making the digital forensic investigation process more complicated. The easy access to sophisticated image editing software and AI-generated materials has brought up the issue of information integrity, the reliability of legal evidence, and public trust. Traditional image forensics methods are usually concerned with either the detection of visual artifacts based on convolutional neural networks (CNNs) or based on metadata analysis, frequently independently of each other. This paper presents a multi-modal fusion paradigm, comprising visual feature-based feature extraction and metadata inconsistency-based detectors, to improve the classification strength. A two-stream design is used, comprising a high-level visual artifact capturing the transfer learning-based MobileNetV2 network and an XGBoost classifier that analyses EXIF metadata discrepancies. The heterogeneous representations are merged in a feature-level fusion strategy to generate a final authenticity prediction. It was tested on individual datasets and a compiled dataset of 26,023 images from CoMoFoD, CG-1050 and CASIA v1 and v2. The suggested approach had an overall accuracy of 83.85%, which was higher than the visual-only (68.61%) and metadata-only (75.85%) baselines. These findings show that complementary visual and metadata cues are much more useful in detection, while the use of a lightweight backbone enables efficient, high-throughput forensic analysis suitable for real-world deployment.
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
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
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
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.
Rapelli Srikanth, Suresh Kumar Mandala· International journal of pat...· 0 citations
ABSTRACT:
Digital images are widely used in social media, journalism, legal evidence, and scientific applications. The availability of advanced image editing tools has increased the risk of digital image forgery, which can lead to misinformation, privacy issues, security threats, and legal complications. Therefore, detecting image forgery accurately has become an important challenge in digital forensics. This paper presents an Image Forgery Detection System using Machine Learning techniques. The proposed system combines Error Level Analysis (ELA) with a Convolutional Neural Network (CNN) to identify manipulated images. ELA is used to highlight inconsistent compression patterns and possible tampered regions, while the CNN learns important visual features for classification. The system classifies uploaded images as either authentic or forged and provides a confidence score along with the prediction. A Python-based Flask backend is used for image processing and model integration, while HTML and CSS provide a simple and user-friendly interface. The results reported in the project demonstrate more than 90% accuracy on the test dataset. The proposed system reduces manual inspection and provides a faster approach for image authenticity verification. The system can be useful in digital forensics, media verification, security, and other applications where image authenticity is important. Future enhancements can include multi-class forgery classification, real-time detection, and cloud-based deployment.Top of Form
M. Tharani, G. Jayanth· International Scientific Jou...· 0 citations
Artificial intelligence has significantly improved digital image editing capabilities, making it increasingly difficult to distinguish authentic images from manipulated ones [5, 7]. This paper proposes a Vision Transformer (ViT)-based framework for digital image forgery detection and localization by leveraging global contextual feature learning [4]. Unlike conventional Convolu-tional Neural Networks (CNNs), Vision Transformers capture long-range dependencies through self-attention mechanisms, enabling more effective identification of manipulated regions [4, 9]. The proposed framework performs image preprocessing, patch extraction, positional encod-ing, transformer-based feature learning, binary classification, and forgery localization. The model is evaluated using publicly available benchmark datasets, including CASIA V2, Co-MoFoD, and FaceForensics++ [20, 48], and its performance is assessed using Accuracy, Pre-cision, Recall, F1-score, Area Under Curve (AUC), Intersection over Union (IoU), and Pixel Accuracy [17, 49]. Experimental results demonstrate that the proposed Vision Transformer framework outperforms conventional CNN-based methods in terms of detection accuracy and localization precision [16, 19]. The proposed approach provides a robust and scalable solution for modern digital image forensics [15] and can be extended to hybrid transformer architectures and video forgery detection in future work.
G. Mary Pushpa, Dr. K. Sravan Adbhilash· International Journal of Lat...· 0 citations
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