Abstract
Neural networks have been applied in various fields, including steganography (called neural network steganography). The network used for secret data extraction is called the extractor. This paper proposes a neural network steganography scheme using extractor matching. In our scheme, the extractor is a publicly available normal network possessed by the receiver, which is used for conventional intelligent tasks. Sender connects extractor to another neural network (called cover network), and then trains the connected network to guarantee correctly data extraction without decreasing the performance of the original task of cover network. During the process of training, the parameters of extractor remain unchanged. Specifically, these network parameters are obtained using an extraction key. The receiver can correctly extract secret data with the help of correct extraction key, while an incorrect key will fail to extract secret data. The feasibility of our scheme is demonstrated in experiments.
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Acknowledgment
This work was supported in part by the Natural Science Foundation of China under Grants 62376148 and 62002214, and supported in part by the Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission under Grant 22CGA46.
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Xie, Y., Wang, Z. (2024). Neural Network Steganography Using Extractor Matching. In: Ma, B., Li, J., Li, Q. (eds) Digital Forensics and Watermarking. IWDW 2023. Lecture Notes in Computer Science, vol 14511. Springer, Singapore. https://doi.org/10.1007/978-981-97-2585-4_12
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DOI: https://doi.org/10.1007/978-981-97-2585-4_12
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