  Recent research has shown that non-additive image steganographic frameworks
effectively improve security performance through adjusting distortion
distribution. However, as far as we know, all of the existing non-additive
proposals are based on handcrafted policies, and can only be applied to a
specific image domain, which heavily prevent non-additive steganography from
releasing its full potentiality. In this paper, we propose an automatic
non-additive steganographic distortion learning framework called MCTSteg to
remove the above restrictions. Guided by the reinforcement learning paradigm,
we combine Monte Carlo Tree Search (MCTS) and steganalyzer-based environmental
model to build MCTSteg. MCTS makes sequential decisions to adjust distortion
distribution without human intervention. Our proposed environmental model is
used to obtain feedbacks from each decision. Due to its self-learning
characteristic and domain-independent reward function, MCTSteg has become the
first reported universal non-additive steganographic framework which can work
in both spatial and JPEG domains. Extensive experimental results show that
MCTSteg can effectively withstand the detection of both hand-crafted
feature-based and deep-learning-based steganalyzers. In both spatial and JPEG
domains, the security performance of MCTSteg steadily outperforms the state of
the art by a clear margin under different scenarios.
