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A Dual-channel Augmented Attentive Dense-convolutional Network for power image splicing tamper detection

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Abstract

Power image tampering brings certain security risks to the safe operation of power grids, among which splicing tampering is the most common. Although image tampering detection has received much attention in recent years, relatively little research has been conducted on the practical application to power systems, and the detection results are poor due to the difficulty of learning subtle edge features of tampered regions and the lack of power image tampering dataset for training. In order to effectively detect image tampering regions, this paper proposes a splicing tampering detection model with a Dual-channel Augmented Attentive Dense-convolutional Network (DAAD-Net) structure. The model consists of three main parts: backbone network feature extraction, augmented attention feature extraction, and tampered region detection. Firstly, the backbone network feature extraction module fuses the original tampered image features with the image residual features and transfers them into the backbone network to extract the feature map. Secondly, the augmented attention feature extraction module extracts the tampered region features in the higher and lower layers using hierarchical encoding and decoding operations. Finally, the extracted feature maps of each layer are sent to the tampered region detection module, and by combining the loss of each feature map for optimizing the network parameters. Additionally, we produced a power image tampering dataset containing 552 samples. Experiments demonstrate that the proposed method outperforms current state-of-the-art models, with 1% to 31% improvement in evaluation indicators, and has good robustness to noise and JPEG compression attacks.

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Data Availability

The data supporting the findings of this work are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No.61772327), State Grid Gansu Electric Power Company (No.H2019-275).

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Authors and Affiliations

  1. College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, 200090, China

    Jianhao Xing & Xiuxia Tian

  2. College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, 200090, China

    Yi Han

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  1. Jianhao Xing
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  2. Xiuxia Tian
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Correspondence to Xiuxia Tian.

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Xing, J., Tian, X. & Han, Y. A Dual-channel Augmented Attentive Dense-convolutional Network for power image splicing tamper detection. Neural Comput & Applic 36, 8301–8316 (2024). https://doi.org/10.1007/s00521-024-09511-6

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