基于自适应哈夫曼编码的密文可逆信息隐藏算法

Reversible Data Hiding in Encrypted Images Using Adaptive Huffman Encoding Strategy

随着云存储和隐私保护的发展,密文域可逆信息隐藏作为一种可以在密文中嵌入秘密信息,保证嵌入后的信息可以无错误提取,并能无损恢复原始明文图像的技术,越来越受到人们的关注.本文提出了一种基于自适应哈夫曼编码的密文域可逆信息隐藏算法,对不同的图像采用不同的哈夫曼码字编码腾出空间来嵌入秘密信息.首先利用自然图像相邻像素间的相关性对原始明文图像进行像素值预测,从最高有效位到最低有效位,对原始像素值和预测像素值的相同比特位进行自适应的哈夫曼编码标记.然后,利用流密码对原始明文图像进行加密.最后在腾出的空间,通过位替换来自适应的嵌入秘密信息.由于哈夫曼编码和解码的可逆性,合法接收者可以对原始明文图像和秘密信息实现分离的无损恢复和提取.实验结果表明,与现有的几种方法相比,本文提出的方法具有更好的安全性和更高的嵌入率,在BOSSBase、BOWS-2和UCID三个数据集上的平均嵌入率比MPHC算法分别提高了0.09bpp、0.062 bpp和0.06bpp,在最佳情况下比MPHC算法能分别高出0.958 bpp、0.797 bpp和0.320 bpp,最差情况下的嵌入率比MPHC算法也分别高出了 0.01 bpp、0.039 bpp和0.061 bpp.

With the growing demand of cloud storage for user privacy protection,RDHEI(Reversible Data Hiding in Encrypted Images),as a technology that can embed secret information in encrypted domain,has attracted more and more attention.A good RDHEI method expects to find the best balance between the number of erroneous extracted bits of the secret information,the embedding rate and the quality of the reconstructed image after data-extraction.General RDHEI methods ensure that the embedded secret information can be extracted without error and the original plaintext image can be restored losslessly,thus the embedding rate is the key index to evaluate the performance of an RDHEI method.This paper proposes an effective reversible data hiding method in encrypted images via an adaptive Huffman Encoding strategy,which utilizes diverse Huffman codewords for various images to free up space to accommodate secret information.The proposed method follows EPE-HCRDH(High-Capacity Reversible Data Hiding with Embedded Prediction Errors) method and is an improved method based on MPHC(multi-MSB Prediction and Huffman Coding) method,which provides a high-security level to protect the original image content.Firstly,by exploiting the correlation between the pixels of a natural image,each pixel can be predicted by its neighbors,so as to obtain the entire prediction image.Next,from MSB(Most Significant Bit) to LSB(Least Significant Bit),the same number of bits between each pair of original and predicted pixels is identified and stored in a label map.Then,the label map is compressed by adaptive Huffman encoding with diverse codewords for various images.Using an encryption key,the original plaintext image is encrypted with stream cipher,and the compressed label map is embedded into encrypted image.Finally,according to the extracted label map,after using a data-hiding key,multi-bit secret information can be embedded adaptively in each encrypted pixel through multi-MSB substitution.Due to the reversibility of Huffman encoding and decoding,the secret information can be extracted error-free and the original plaintext image can be restored losslessly by MSB prediction.For different keys,image-recovery and data-extraction can be performed separately.Compared with the experimental results of several state-of-the-art methods,the proposed method has better security performance and achieves higher embedding rate.The average embedding rate of the proposed method outperforms MPHC method 0.09 bpp,0.062 bpp and 0.06 bpp on three datasets BOSSBase,BOWS-2 and UCID,respectively.In addition,the texture complexity of the original plaintext image has a significant effect on the embedding rate.Generally speaking,smooth images have a satisfactory embedding rate,while texture images have a less ideal embedding rate.For both smooth images and texture images,the proposed method achieves higher embedding rate and outperforms the competitors.On the three datasets,the embedding rate of the proposed method is 0.958 bpp,0.797 bpp,0.320 bpp higher than MPHC method in the best case,and 0.01 bpp,0.039 bpp,0.061 bpp higher than MPHC method in the worst case,respectively.It is shown that the proposed method of adaptive Huffman codewords encoding has better performance than the MPHC method of predefined Huffman codewords encoding.