Jul 2026· IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies· pp. 303-309· 0 citations· 13 references
Abstract
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.
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
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.
Baysah Guwor, Mohammad Shabaz· Multimedia· 0 citations
The popularity of digital image editing techniques has made image forgery detection increasingly important. Existing methods suffer from insufficient robustness and generalization ability, as well as poor synergy between detection and localization tasks. This paper proposes an image forgery detection and localization method based on Multilevel Feature Collaboration Bridge (Loupe). Using Vision Transformer as the backbone to extract multi-level features, we design a Feature Collaboration Bridge Network to realize efficient information interaction between image-level classification and pixel-level segmentation branches via feature concatenation, attention fusion and gating mechanism. The model has only 6.6M parameters, with F1-score of 0.9110 and AUC of 0.9623, outperforming mainstream baseline models. The effectiveness of each component is verified by ablation experiments. Extensive tests under compression and noise interference verify its excellent robustness and generalization, making it suitable for complex real-world scenarios.
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
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
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