Detecting Image Forgeries Using Hybrid Residual-Dense Swin Transformer Framework with Attention-Based U-Net

Abstract

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.