Abstract The importance of image steganography is unquestionable in the field of secure multimedia communication. Imperceptibility and high payload capacity are some of the crucial parts of any mode of steganography. The proposed work is an attempt to modify the edge-based image steganography which provides higher payload capacity and imperceptibility by making use of machine learning techniques. The approach uses an adaptive embedding process over Dual-Tree Complex Wavelet Transform (DT-CWT) subband coefficients. Machine learning based optimization techniques are employed here to embed the secret data over optimal cover-image-blocks with minimal retrieval error. The embedding process will create a unique secret key which is imperative for the retrieval of data and need to be transmitted to the receiver side via a secure channel. This enhances the security concerns and avoids data hacking by intruders. The algorithm performance is evaluated with standard benchmark parameters like PSNR, SSIM, CF, Retrieval error, BPP and Histogram. The results of the proposed method show the stego-image with PSNR above 50 dB even with a dense embedding of up to 7.87 BPP. This clearly indicates that the proposed work surpasses the state-of-the-art image steganographic systems significantly.

中文翻译：

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使用双树复小波变换覆盖区域选择的高容量自适应图像隐写术

摘要 图像隐写术在安全多媒体通信领域的重要性是毋庸置疑的。不可察觉性和高有效载荷能力是任何隐写术模式的一些关键部分。拟议的工作是尝试修改基于边缘的图像隐写术，通过利用机器学习技术提供更高的有效载荷能力和不可察觉性。该方法在双树复小波变换 (DT-CWT) 子带系数上使用自适应嵌入过程。这里采用基于机器学习的优化技术，以最小的检索错误将秘密数据嵌入到最佳覆盖图像块上。嵌入过程将创建一个唯一的密钥，该密钥对于检索数据是必不可少的，并且需要通过安全通道传输到接收方。这增强了安全问题并避免了入侵者的数据黑客攻击。使用标准基准参数（如 PSNR、SSIM、CF、检索误差、BPP 和直方图）评估算法性能。所提出的方法的结果表明，即使密集嵌入高达 7.87 BPP，隐写图像的 PSNR 也高于 50 dB。这清楚地表明，所提出的工作显着超越了最先进的图像隐写系统。