差异聚类和误差纹理合成的生成式信息隐藏

Generation information hiding method combining difference clustering and error texture synthesis

目的搜索式无载体信息隐藏容量小、搜索量大,涉及大量载体密集传输;纹理构造式隐藏只能生成简单非自然纹理;纹理合成式隐藏存在固定映射以及编码、非编码小块的明显区别特征,且未考虑样本小块差异度和遭受攻击时的类别提取错误,抵抗攻击能力十分有限。针对以上问题,提出一种差异聚类和误差纹理合成的生成式信息隐藏。方法在嵌入时,通过差异均值聚类获取编码样本小块,结合多重映射将代表秘密信息的编码样本小块随机放置在空白图像上,按最小误差优先拼接策略生成含密纹理。在提取时,通过密钥截取样本小块,寻找最接近编码样本小块,并结合秘密信息MD5(message-digest algorithm 5)值和随机坐标来恢复秘密信息。结果所提方法与MD5值和密钥紧密绑定,密钥参数、MD5值以及样例图的改变都将导致秘密信息的提取误码率趋近于0.5。同现有方法相比,结合最小误差优先拼接策略,所提方法的像素累计差异更小,含密纹理视觉质量较好且对密钥极度敏感,以实验样本为例,当遭受质量因子为50~70的JPEG压缩和5%~15%的椒盐噪声攻击时,秘密信息可完整提取。即使遭受25%~40%的椒盐噪声攻击,提取误码率低于7%。结论所提方法避免了固定映射和编码、非编码小块的区别特征,含密掩体视觉质量较好且具有较强的抗攻击能力。

Objective Two typical methods of coverless information hiding are currently available. One is search-based coverless information hiding, which transmits secret information by querying a text or image containing secret information from a database, and the other is texture generation-based information hiding,which relays secret information by generating a stego texture similar to a given image texture. Search-based information hiding has a small embedding capacity and a large search space and involves the intensive transmission of numerous carriers. Although every isolated text or image in this method is a normal text or image without modification, the method is still suspicious because of the dense transmission of carriers. Texture generation-based information hiding can be further divided into texture construction and texture synthesis-based information hiding. Generating a real natural texture directly is challenging. In texture synthesis-based information hiding, apparent distinguishing features between coded and non-coded blocks and a fixed mapping relationship between secret information and coded blocks exist. The method has low security and disregards the difference degree among various coded blocks and category errors during attacks. To address these problems, this work proposes a generation information hiding method that combines difference clustering and minimum error texture synthesis. Method First, in the embedding process, sample blocks are randomly captured in the sample texture image by a key. Then, the mean square errors of the kernel regions between the sample blocks and the random key template are calculated. These errors are divided into several categories through a difference mean clustering strategy, in which the sample block closest to the cluster center position is selected as the coded sample block in each category. Second, a multiple mapping relationship is established to obtain the coded sample block number through secret information decimal numbers, the MD5(message-digest algorithm 5) value of the secret information, random coordinates, and the coded sample blocks. Finally, the coded sample blocks that represent the secret information decimal numbers are placed randomly in a blank image. The nearest sample blocks are selected to cover the secret information and generate a stego texture image through minimum error priority stitching, where the splicing order is determined by the minimum difference among adjacent blocks. This strategy always selects the least difference error line for minimum difference splicing. In the extraction process, all stego blocks are truncated in the stego texture image by the key, and the same coded sample blocks are obtained by difference mean clustering through the given sample image. All of the closest coded block numbers corresponding to the truncated stego blocks are identified via a similarity comparison and used to recover the binary secret information by combining these block numbers with the secret information′s MD5 value and random coordinates. Result The proposed method is tightly bound to the plaintext attribute of the secret information′s MD5 value and the key. The method completely depends on the key, MD5 value, and sample texture image. Only the correct key, MD5 value, and sample texture image can completely recover secret information. Any change or changes in individual or multiple key variables and the texture sample image will result in errors. For example, the EBR(error bit rate) of the extracted secret information could approach 0.5, and half of the extracted secret information bits cannot be fetched with the maximum uncertainty. Through the minimum error priority, the proposed method has a smaller pixel cumulative difference on the minimum error line compared with existing methods, and the generated stego texture image has better visual quality and is extremely sensitive to the key. The visual quality of stego texture maps decreases whether in salt-and-pepper noise, graffiti, or JPEG compression. However, during high-intensity salt-and-pepper noise and large-scale graffiti attacks, most of the bits embedded into the secret information can be accurately extracted or even completely fetched. In the given experiment samples, the quality factors are set from 50 to 70 for JPEG compression attacks, the EBR of the recovered secret information is always 0, and the entire secret information is completely restored. For 5% to 15% salt-and-pepper noise attacks, the EBR of the recovered secret information is still 0, and the secret information can be completely fetched. Even under 25% to 40% high-intensity salt-and-pepper noise attacks, the EBR of the extracted secret information remains very low, that is, less than 7%. Thus, the proposed method has a strong attack tolerance to high-intensity salt-and-pepper noise and large-scale graffiti attacks. The method can also resist low-quality JPEG compression attacks. Conclusion The proposed method does not require many samples to build a large database. It avoids the retrieval of big data, and its computation cost is small. The proposed method only involves single carrier embedding, and its embedding capacity is high. It can produce high-quality texture to cover secret information. The introduced random key template and the established multiple mapping relationship between random coordinates and coded sample blocks avoid the fixed mapping relationship between secret information and coded sample blocks. The coded sample blocks have the largest inter-class difference because of sample difference mean clustering. Therefore, the proposed method has a robust recovery process that is entirely dependent on the key, and its security is high. The splicing order is determined according to the minimum difference among adjacent blocks, and the least difference error line that can cover the secret information with high quality is selected for splicing. Moreover, difference minimum error line splicing that can cover secret information with high quality is selected.