Adaptive imagesteganography embeds secret messages into areas of cover images with complex features, including rich edges and complex textures. In this article, an adaptive image steganography technique based on the edge and complex texture areas of images is proposed, by comprehensively considering three rules in the design of image steganography. First, the embedding area is composed of the edge and complex texture areas of images, according to the complexity-first rule. Edge detection is realized by an improved fuzzy enhancement function, optimized by the grey wolf optimizer to detect both the weak and strong edges. Second, the minimum average classification error rate is used to assess the choice of the complex texture areas. Third, under the spreading rule, two different average filters and one KerBohme filter are used to design the cost function in the embedding areas. Finally, confidential information is adaptively embedded through syndrome-trellis codes. Experimental results show that the proposed algorithm outperforms seven classical adaptive image steganography algorithms on two steganalytic feature sets. The performance improvement is particularly significant when the payload is large.

中文翻译：

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使用模糊增强和灰狼优化器的自适应图像隐写术


自适应图像隐写术将秘密消息嵌入到具有复杂特征（包括丰富边缘和复杂纹理）的封面图像区域中。本文综合考虑图像隐写设计中的三个规则，提出了一种基于图像边缘和复杂纹理区域的自适应图像隐写技术。首先，根据复杂性优先的规则，嵌入区域由图像的边缘和复杂纹理区域组成。边缘检测是通过改进的模糊增强函数实现的，并由灰狼优化器优化以检测弱边缘和强边缘。其次，使用最小平均分类错误率来评估复杂纹理区域的选择。第三，在扩展规则下，使用两个不同的平均滤波器和一个KerBohme滤波器来设计嵌入区域的成本函数。最后，机密信息通过综合症网格代码自适应地嵌入。实验结果表明，该算法在两个隐写分析特征集上优于七种经典的自适应图像隐写算法。当有效负载较大时，性能提升尤其显着。