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MITD-Net: Multi-scale iterative tamper detection network for image manipulation localization
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AI
DOI：10.1016/j.dsp.2024.104901.png)
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Abstract
En 中文
Advancements in image tampering technology have escalated social risks and security challenges associated with tampered images. Accurately identifying tampered regions remains a significant challenge. To address the issue effectively, a multi-scale iterative tampering detection network is proposed, which decomposes the task into two phases, the Global-Local Feature Synchronization Phase and the Tampered Area Refinement Phase. Initially, dual-branch preprocessing and parallel feature extraction are employed. The Enhanced Downsampling Attention Block (EDAB) is introduced, and an Edge Enhancement Module (EEM) is implemented to extract edge features. Subsequently, Multi-Scale Dilated Convolution (MSDC) is utilized in the tampered area refinement phase, along with the proposed Pixel-level Feature Clustering Module (PFCM) and an iterative mechanism. Finally, incorporating a cross-scale fusion mechanism enables the synthesis of feature information from various scales, thereby enhancing the model's ability to detect subtle alterations in tampered regions. MITD-Net was validated on publicly available datasets, including CASIA, Columbia, COVERAGE, NIST 16, and IMD 20, achieving AUC scores of 84.5%, 98.5%, 85.8%, 86.6%, and 82.6%, respectively. Numerous experiments indicate that MITD-Net achieves higher detection accuracy, exhibits superior robustness, and can adapt to various tampering types and styles.
Keywords:
Image manipulation localization
Attention mechanism
Multi-scale supervision
Multi-view feature learning
Edge distribution guidance
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