UGEE-Net: Uncertainty-guided and edge-enhanced network for image splicing localization

Image splicing, a prevalent method for image tampering, has significantly undermined image authenticity. Existing methods for Image Splicing Localization (ISL) struggle with challenges like limited accuracy and subpar performance when dealing with imperceptible tampering and multiple tampered regions. We introduce an Uncertainty-Guided and Edge-Enhanced Network (UGEE-Net) for ISL to tackle these issues. UGEE-Net consists of two core tasks: uncertainty guidance and edge enhancement. We employ Bayesian learning to model uncertainty maps of tampered regions, directing the model's focus to challenging pixels. Simultaneously, we employ a frequency domain-auxiliary edge enhancement strategy to imbue localization features with global contour information and fine-grained local details. These mechanisms work in parallel, synergistically boosting performance. Additionally, we introduce a cross-level fusion and propagation mechanism that effectively utilizes contextual information for cross-layer feature integration and leverages channel-level correlations for cross-layer feature propagation, gradually enhancing the localization feature's details. Experiment results affirm UGEE-Net's superiority in terms of detection accuracy, robustness, and generalization capabilities. Furthermore, to meet the growing demand for high-quality datasets in image forensics, we present the HTSI12K dataset, which includes 12,000 spliced images with imperceptible tampering traces and diverse categories, rendering it suitable for realworld auxiliary model training.