Bridging Restoration and Forensics: A Novel Framework for Image Tampering Detection

With the widespread manipulation of digital images, accurately detecting and localizing tampered regions has become a critical task in image forensics. However, most existing tampering localization methods struggle when images undergo post-processing operations such as compression, which obscure crucial tampering traces. To address this, we propose a ranking-based tampering detection framework that evaluates image authenticity by assigning ranks, calculating distances between images, and identifying possible tampering. Our system processes images by extracting key features and assigning a rank based on specific parameters. The framework then measures the distance between different images, helping determine the extent of modifications and detect potential forgeries. Additionally, to enhance tampering localization, we integrate a restoration module that refines the quality of processed images, improving the detection of altered regions. Unlike conventional methods, our approach not only identifies tampered areas but also assesses the degree of alteration through a structured ranking mechanism. To validate our method, we implemented extensive experiments using a variety of datasets, evaluating the framework’s accuracy in detecting tampered images. The results demonstrate that our approach significantly improves the robustness of tampering detection, particularly under image compression and other post-processing effects. Furthermore, the ranking-based distance calculation method enhances the ability to differentiate between authentic and manipulated images, making our system an effective tool for real-world image forensics applications.