ALL-IN-ONE: Divide-and-Conquer Strategy for Multi-Manipulation Image Classification and Localization

In the advertising and media industries, image editing often involves multiple manipulation techniques to meet creative and technical requirements. Detecting tampered regions is crucial in scenarios like legal disputes or media integrity assessments. However, existing forensic methods often target single manipulation types or treat all manipulations as one, and many deep learning approaches lack flexibility in frequency and edge extraction, limiting their effectiveness. To address these challenges, this paper proposes an ALL-IN-ONE framework for comprehensive image forensic analysis, which adopts a divide-and-conquer strategy for multi-manipulation image classification and localization. Specifically, we introduce a Multi-Frequency Band Extraction Module (MBEM) to capture richer artifact information in the frequency domain. This is complemented by an Attention Window-based Fusion Module, which fuses same-frequency features across different scales and enhances the discriminative features more effectively. To improve the localization of copy-move manipulation, we design a Copy-Move Accurate Detection Module (CADM), which leverages the visual consistency between source and target regions. Furthermore, we propose a Precise Edge Generator (PEG) as part of the Edge-Guided Progressive Fine-Tune Module (EPFM), which can generate more accurate edge to enhance edge localization. To address the issue of insufficient labeled data, we construct a publicly available dataset, the Multi-Manipulation Image Dataset (MMID), consisting of 2,000 multi-manipulation images, each containing at least two types of forgeries. Extensive experiments are conducted, comparing our method with state-of-the-art approaches on MMID, as well as on single-manipulation datasets such as CASIA, CoMoFoD, and NIST. The results demonstrate that MMID is effective for training discriminative models and validate that our proposed method significantly outperforms existing approaches in terms of accuracy and robustness for simultaneous forgery localization and manipulation classification.