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Multi-scale and deeply supervised network for image splicing localization
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DOI：10.3389/frai.2025.1655073.png)
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Abstract
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When maliciously tampered images are disseminated in the media; they can potentially cause adverse effects and even jeopardize national security. Therefore; it is necessary to investigate effective methods to detect tampered images. As a challenging task; the localization of image splicing tampering investigates whether an image contains tampered regions spliced from another image. Given the lack of global information interactions in existing methods; a multi-scale; deeply supervised image splicing tampering localization network is proposed. The proposed network is based on an encoder–decoder architecture; where the decoder uses different levels of feature maps to supervise the locations of splicing; enabling pixel-wise prediction of tampered regions. Moreover; a multi-scale feature extraction module is utilized between the encoder and decoder; which expands the global view of the network; thereby enabling more effective differentiation between tampered and non-tampered regions. F1 scores of 0.891 and 0.864 were achieved using the CASIA and COLUMB datasets; respectively; and the proposed model was able to accurately locate tampered regions.
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