TBNet:Two-Stream Boundary-aware Network for Generic Image Manipulation Localization

• URL: http://arxiv.org/abs/2108.04508v1
• Date: Tue, 10 Aug 2021 08:22:05 GMT
• Title: TBNet:Two-Stream Boundary-aware Network for Generic Image Manipulation Localization
• Authors: Zan Gao, Chao Sun, Zhiyong Cheng, Weili Guan, Anan Liu, Meng Wang
• Abstract summary: We propose a novel end-to-end two-stream boundary-aware network (abbreviated as TBNet) for generic image manipulation localization. The proposed TBNet can significantly outperform state-of-the-art generic image manipulation localization methods in terms of both MCC and F1.
• Score: 49.521622399483846
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Finding tampered regions in images is a hot research topic in machine learning and computer vision. Although many image manipulation location algorithms have been proposed, most of them only focus on the RGB images with different color spaces, and the frequency information that contains the potential tampering clues is often ignored. In this work, a novel end-to-end two-stream boundary-aware network (abbreviated as TBNet) is proposed for generic image manipulation localization in which the RGB stream, the frequency stream, and the boundary artifact location are explored in a unified framework. Specifically, we first design an adaptive frequency selection module (AFS) to adaptively select the appropriate frequency to mine inconsistent statistics and eliminate the interference of redundant statistics. Then, an adaptive cross-attention fusion module (ACF) is proposed to adaptively fuse the RGB feature and the frequency feature. Finally, the boundary artifact location network (BAL) is designed to locate the boundary artifacts for which the parameters are jointly updated by the outputs of the ACF, and its results are further fed into the decoder. Thus, the parameters of the RGB stream, the frequency stream, and the boundary artifact location network are jointly optimized, and their latent complementary relationships are fully mined. The results of extensive experiments performed on four public benchmarks of the image manipulation localization task, namely, CASIA1.0, COVER, Carvalho, and In-The-Wild, demonstrate that the proposed TBNet can significantly outperform state-of-the-art generic image manipulation localization methods in terms of both MCC and F1.

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