It is a potential threat to persons and companies to reveal private or company-sensitive data through the Internet of Things by the color image steganography. The existing rich model features for color image steganalysis fail to utilize the fact that the content-adaptive steganography changes the pixels in complex textured regions with higher possibility. Therefore, this article proposes a variant of spatial rich model feature based on the embedding change probabilities in differential channels. The proposed feature is extracted from the residuals in the differential channels to reduce the image content information and enhance the stego signals significantly. Then, the embedding change probability of each element in the differential channels is added to the corresponding co-occurrence matrix bin to emphasize the interference of the residuals in textured regions to the improved co-occurrence matrix feature. The experimental results show that the proposed feature can significantly improve the detection performances for the WOW and S-UNIWARD steganography, especially when the payload size is small. For example, when the payload size is 0.05 bpp, the detection errors can be reduced respectively by 5.20% and 4.90% for WOW and S-UNIWARD by concatenating the proposed feature to the color rich model feature CRMQ1.

中文翻译：

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基于差分通道嵌入变化概率的彩色图像隐写分析

通过彩色图像隐写术通过物联网泄露私人或公司敏感数据对个人和公司构成潜在威胁。现有的用于彩色图像隐写分析的丰富模型特征未能利用内容自适应隐写术更可能改变复杂纹理区域中的像素这一事实。因此，本文提出了一种基于差分通道中嵌入变化概率的空间丰富模型特征的变体。从差分通道中的残差中提取所提出的特征，以减少图像内容信息并显着增强隐写信号。然后，将差分通道中每个元素的嵌入变化概率添加到相应的共生矩阵bin中，以强调纹理区域中的残差对改进的共生矩阵特征的干扰。实验结果表明，所提出的特征可以显着提高 WOW 和 S-UNIWARD 隐写术的检测性能，尤其是在有效载荷较小的情况下。例如，当有效载荷大小为 0.05 bpp 时，通过将提出的特征连接到色彩丰富的模型特征 CRMQ1，WOW 和 S-UNIWARD 的检测误差可以分别降低 5.20% 和 4.90%。实验结果表明，所提出的特征可以显着提高 WOW 和 S-UNIWARD 隐写术的检测性能，尤其是在有效载荷较小的情况下。例如，当有效载荷大小为 0.05 bpp 时，通过将提出的特征连接到色彩丰富的模型特征 CRMQ1，WOW 和 S-UNIWARD 的检测误差可以分别降低 5.20% 和 4.90%。实验结果表明，所提出的特征可以显着提高 WOW 和 S-UNIWARD 隐写术的检测性能，尤其是在有效载荷较小的情况下。例如，当有效载荷大小为 0.05 bpp 时，通过将提出的特征连接到色彩丰富的模型特征 CRMQ1，WOW 和 S-UNIWARD 的检测误差可以分别降低 5.20% 和 4.90%。