Search Results

With The Rapid Advancement Of Digital Editing Tools, Image Manipulation Has Become Increasingly Sophisticated And Widespread, Raising Serious Concerns In Areas Such As Journalism, Law Enforcement, Medical Imaging, And Social Media. Traditional Image Forgery Detection Techniques, Such As Error Level Analysis And Metadata Inspection, Often Fail To Detect Subtle Manipulations Or Perform Poorly On Large-scale Datasets. Deep Learning Has Recently Emerged As A Powerful Solution, Offering Automated Feature Extraction And High Accuracy In Identifying Complex Patterns Of Tampering. This Study Explores The Application Of Deep Learning Models—such As Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), And Hybrid Architectures—for Detecting Common Forgery Types Including Copy-move, Splicing, And Deepfake Images. The Proposed Framework Leverages Spatial And Frequency Domain Analysis, Combined With Transfer Learning, To Enhance Detection Robustness Across Diverse Datasets. Experimental Evaluations Demonstrate That Deep Learning–based Methods Outperform Conventional Approaches In Terms Of Precision, Recall, And Generalization To Unseen Manipulations. The Findings Highlight The Potential Of Deep Learning Models As Reliable And Scalable Tools For Real-world Image Forgery Detection, Paving The Way For Stronger Digital Content Authentication Systems.