Recently, the success of non-additive steganography has demonstrated that asymmetric distortion can remarkably improve security performance compared with symmetric cost functions. However, most of current existing additive steganographic methods are still based on symmetric distortion. In this paper, for the first time we optimize asymmetric distortion for additive steganography and propose an A3C (Asynchronous Advantage Actor-Critic) based steganographic framework, called ReLOAD. ReLOAD is composed of an actor and a critic, where the former guides action selection for pixel-wise distortion modulation, and the latter evaluates the performance of modulated distortion. Meanwhile, a reward function that considers embedding effects is proposed to unify the goal of steganography and reinforcement learning, so that the minimization of embedding effects can be achieved by learning secure policy to maximize total rewards. Statistical analysis shows that compared with non-additive steganography, ReLOAD achieves lower change rates and makes embedding traces more consistent with cover image textures. Comprehensive experiments conducted on both hand-crafted feature-based and deep learning-based steganalyzers show that ReLOAD significantly promotes the state-of-the-art security performance of current additive methods and even outperforms non-additive steganography when the modification distribution gets sparser.

中文翻译：

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ReLOAD：使用强化学习优化加法隐写术的不对称失真


最近，非加性隐写术的成功表明，与对称成本函数相比，不对称失真可以显着提高安全性能。然而，目前大多数现有的加性隐写方法仍然基于对称失真。在本文中，我们首次优化了加性隐写术的非对称失真，并提出了一种基于 A3C（异步优势参与者-评论家）的隐写框架，称为 ReLOAD。 ReLOAD由演员和评论家组成，前者指导像素级失真调制的动作选择，后者评估调制失真的性能。同时，提出了考虑嵌入效应的奖励函数，以统一隐写术和强化学习的目标，从而通过学习安全策略来实现嵌入效应的最小化，从而最大化总奖励。统计分析表明，与非加性隐写术相比，ReLOAD 实现了更低的变化率，并使嵌入痕迹与封面图像纹理更加一致。在手工制作的基于特征的隐写分析器和基于深度学习的隐写分析器上进行的综合实验表明，ReLOAD 显着提升了当前加法方法的最先进的安全性能，甚至在修改分布变得稀疏时优于非加法隐写术。