Perceptual Authentication Hashing with Image Feature Fusion Based on Window Self-Attention

Zhou Yuanding; Gao Guopeng; Fang Yaodong; Qin Chuan

doi:10.7544/issn1000-1239.202330669

Journal of Computer Research and Development > 2025 > Accepted Manuscript > DOI: 10.7544/issn1000-1239.202330669 CSTR: 32373.14.issn1000-1239.202330669

Zhou Yuanding, Gao Guopeng, Fang Yaodong, Qin Chuan. Perceptual Authentication Hashing with Image Feature Fusion Based on Window Self-Attention[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202330669

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Perceptual Authentication Hashing with Image Feature Fusion Based on Window Self-Attention

School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093

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Author Bio:
Zhou Yuanding: born in 1997. PhD. His main research interests include deep perceptual hashing and multimedia information processing

Gao Guopeng: born in 1998. Master. His main research interest includes perceptual image hashing

Fang Yaodong: born in 1997. Master. His main research interests include perceptual image hashing and image authentication

Qin Chuan: born in 1980. PhD, professor, PhD supervisor, Senior member of CCF. His main research interests include multimedia information security and AI security
Received Date: August 20, 2023
Accepted Date: January 25, 2025
Available Online: January 25, 2025

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Abstract

Abstract

With the rapid development of multimedia and network technology, the security of digital image content is becoming more and more prominent. In this paper, we propose a deep perceptual image authentication hashing schema based on window self-attention feature fusion, that can effectively detect whether the perceptual content of the original image has changed. It can be applied to content authentication, tampering recognition, copy detection, and other similar scenarios. This model uses a convolutional neural network architecture that integrates a window self-attention mechanism to build a hashing model that encompasses global and local image features. The model chunks the shallow features obtained from the backbone network and extracts the corresponding window features, then calculates the correlation between each intermediate local feature and the global feature to filter out the final local features, and finally inputs the local features and global features into the hash generation module for fusion and compression to obtain the final image hash code. In the training process, an integrated loss function based on hash loss and classification loss is used to constrain the model to improve the robustness and discrimination. The experimental results show that this scheme can achieve superior image content authentication performance compared with existing typical perceptual authentication hashing schemes.
- perceptual hashing,
- image authentication,
- window self-attention,
- feature fusion,
- robustness

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References (30)

References

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