A Generative Steganography Based on Attraction-Matrix-Driven Gomoku Games

Generative steganography uses generative stego images to transmit secret message. It also effectively defends against statistical steganalysis. However, most existing methods focus primarily on matching the feature distribution of training data, often neglecting the sequential continuity between moves in the game. This oversight can result in unnatural patterns that deviate from real user behavior, thereby reducing the security of the hidden communication. To address this issue, we design a Gomoku agent based on the AlphaZero algorithm. The model engages in self-play to generate a sequence of plausible moves. These moves form the basis of the stego images. We then apply an attraction matrix at each step. It guides the move selection so that the moves appear more natural. This method helps maintain logical flow between moves. It also extends the game length, which increases the embedding capacity. Next, we filter and prioritize the generated moves. The selected moves are embedded into a move pool. Secret message is mapped to these moves. It is then embedded step by step as the game progresses. The final move sequence constitutes a complete steganographic game record. The receiver can extract the secret message using this record and a predefined mapping rule. Experiments show that our method reaches a maximum embedding capacity of 223 bits per carrier. Detection accuracy is 0.500 under XuNet and 0.498 under YeNet. These results are equal to random guessing, showing strong imperceptibility. The proposed method demonstrates superior concealment, higher embedding capacity, and greater robustness against common image distortions and steganalysis attacks.