Aug 2026· Journal of Intelligent Decision Making and Information Science· 0 citations· 24 references
TL;DR
The paper suggests a hybrid training system and adaptive thresholding to improve the generalization of cross-datasets in image forgery detection and indicates that mixed-domain training is a practical approach that can reduce dataset bias and increase generalization.
Abstract
Image forgery detection is an important part of digital forensics especially as more and more sophisticated image manipulating tools become more and more accessible. Despite the fact that deep learning models have shown high accuracy on benchmark datasets, their performance on heterogeneous datasets is a major challenge because of the domain shift and changes in manipulation methods. The paper suggests a hybrid training system and adaptive thresholding to improve the generalization of cross-datasets in image forgery detection. A model trained on CASIA dataset performed well on in-domain data but has a significant decrease when tested on unseen data with only a 48.5% accuracy on the COVERAGE dataset. To overcome this weakness, a mixed-domain learning approach that used samples across various datasets such as CASIA and COVERAGE was proposed in order to enhance feature robustness. The suggested solution was tested using three benchmark data sets, including CASIA, Columbia and COVERAGE. The experimental results show high accuracy of CASIA (approximately 95%), Columbia (approximately 95%), and considerably better accuracy on COVERAGE at 91.4%. The model also obtained high AUC-ROC value of 0.994 which implies that there was great class separability. Further analysis based on the precision, recall and F1-score validates balanced and consistent performance. Moreover, qualitative analysis in terms of score distribution and prediction visualization depicts obvious discrimination between genuine and manipulated photos. The results indicate that mixed-domain training is a practical approach that can reduce dataset bias and increase generalization, which is why the proposed framework can be adopted in the real-world forensic use. The work offers a complete solution to domain adaptation issue in the image forgery detection.
The increasing accessibility of digital image editing tools has made image manipulation easier than ever before, creating significant challenges in verifying the authenticity of digital visual content. Copy-move forgery is one of the most widely used image manipulation techniques because duplicated regions originate from the same image, making manual detection difficult. Existing forgery detection approaches often rely on handcrafted feature extraction or a single deep learning architecture, which may exhibit reduced performance when images undergo transformations such as rotation, scaling, compression, illumination variation, and noise addition. This paper presents a fusion-based lightweight deep learning framework for copy-move image forgery detection and localization. The proposed system integrates three efficient convolutional neural network architectures, namely SqueezeNet, MobileNetV2, and ShuffleNet, to extract complementary deep image features. Instead of depending on a single classifier, the extracted features are fused and classified using a Support Vector Machine (SVM), resulting in improved prediction reliability and reduced false detection. To provide visual evidence of manipulation, the framework incorporates a Discrete Cosine Transform (DCT)-based localization module that identifies duplicated image regions and highlights forged areas. The proposed model was implemented using Python with TensorFlow, OpenCV, Scikit-learn, NumPy, and Tkinter. Experimental evaluation was conducted on the MICC-F220 benchmark dataset. The fusion strategy achieved an overall detection accuracy of 90.91%, outperforming individual lightweight convolutional neural network models while maintaining low computational complexity. The proposed framework 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
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
This work explores an approach that integrates wavelet-based frequency analysis with deep learning to enhance deepfake detection, and suggests that wavelet sub-bands expose manipulation cues that are useful for detecting unseen fake classes, but they should not be interpreted as a uniform robustness improvement.
Face forgery detectors often achieve strong results on controlled benchmarks, but their reliability under realistic image degradations remains limited. This paper presents a standardized benchmark for face forgery detection using the Multi-Dimensional Face Forgery Image (MFFI) dataset and evaluates performance on both clean and degraded test partitions. We compare six model families, including convolutional networks, transformer-based models, and a frozen self-supervised DINOv3 backbone, across spatial, spectral, and hybrid input representations. The results show that clean-set performance is not a reliable indicator of robustness under compression, resizing, and blurring. Xception with RGB obtains the best clean performance, reaching 0.884 mean ROC-AUC, but degrades substantially on the harder partition. In contrast, frozen DINOv3 achieves the strongest degraded-set result, with 0.726 mean ROC-AUC, while training only a linear classification head. The representation analysis indicates that Fourier-domain cues are most useful when combined with RGB information, whereas purely spectral inputs consistently underperform spatial representations. Qualitative attribution maps further suggest that convolutional detectors focus on localized artifacts, while DINOv3 relies on broader facial structure. These findings reinforce the need for degraded evaluation protocols and highlight self-supervised visual representations as a promising direction for robust face forgery detection. Our source code is publicly available at https://github.com/lucasdocunha/FaceForgery-Benchmark/.
Lucas Cunha, Lucas Sotomaior, Lucas Gasperin et al.· 0 citations
Experimental results demonstrate that the proposed approach effectively identifies deepfake images with high accuracy, making it suitable for applications in digital forensics, media verification, and cybersecurity.
J. Kollu, Mortha Pavan, Putta Vardhan et al.· International Journal of Inn...· 0 citations
Overall, GenPix provides a challenging and realistic benchmark for evaluating modern detectors, and the proposed AAE offers an efficient, interpretable baseline for future research on general-purpose fake-image detection.
Guessoum Dalila, B. Nadjia, Boumahdi Fatima et al.· Iraqi Journal for Computer S...· 0 citations
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