Abstract
The structure of the Generative Adversarial Network (GAN) has demonstrated good performance in various tasks, mainly comprising two competing sub-networks. The GAN has the potential to effectively generate artificial samples that closely resemble the actual sample distribution. The field of steganography utilizing the Generative Adversarial Network (GAN) structure has witnessed a wealth of research with highly successful outcomes. This paper proposes a steganography framework that integrates reinforcement learning and introduces a new reward function to analyze the embedding cost of images in the steganography problem. In this framework, the reward function assigns distortion values to each pixel of the image and relates the security performance of steganography. Based on the conducted experiments, an enhanced steganographic embedding scheme can ultimately be achieved.
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Acknowledgements
This work was supported by NSFC (Grant 62002075), Guangdong Basic and Applied Basic Research Foundation (Grant 2023A1515011428), the Science and Technology Foundation of Guangzhou (Grant 2023A04J1723).
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Tang, W., Xie, Y. (2024). An Embedded Cost Learning Framework Based on Cumulative Gradient Rewards. In: Vaidya, J., Gabbouj, M., Li, J. (eds) Artificial Intelligence Security and Privacy. AIS&P 2023. Lecture Notes in Computer Science, vol 14509. Springer, Singapore. https://doi.org/10.1007/978-981-99-9785-5_19
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