Focused on the problems of low hiding capacity and poor quality of decrypted labeled images in the existing Reversible Data Hiding in Encrypted Image （RDHEI） methods， a new RDHEI method based on high-order bit-plane redundancy was proposed. Firstly， the original image was encrypted in blocks by Logistic mapping， and the redundancy of the high-order bit-plane of the pixels in the blocks was retained. Secondly， according to the rule of whether the numbers of high-order bits and low-order bits in the block were the same， the encrypted image blocks were divided into embeddable blocks and non-embeddedable blocks， and the low-order bit value of the pixel was replaced with the corresponding high-order bit value in the embeddable blocks， so that the high-order bit-plane redundancy was transferred to the low-order bit-plane. Finally， the confidential information was embedded in the embedding space vacated in the inner-block low-order bit-plane. After that， the operations of data extraction， image decryption and image lossless recovery were realized by the receiver with the key. In the simulation experiments on 6 images in the USC-SIPI standard image library， when the number of high-order bit-planes is equal to 3， the proposed method has the average embedding rate of the image of 1.73 bpp， and the average Peak Signal-to-Noise Ratio （PSNR） of the marked image after direct decryption reaches 47.20 dB. The experimental results show that the proposed method not only increases the information embedding capacity of the encrypted image， but also increases the PSNR value of the labeled image after direct decryption.

The double quantization effect of JPEG (Joint Photographic Experts Group) provides important clues for detecting image tampering. When an original JPEG image undergoes localized tampering and is saved again in JPEG format, the Discrete Consine Transform (DCT) coefficients of untampered regions would undergo double JPEG compressing, while the DCT coefficients of tampered regions would only undergo a single compression. The Alternating Current (AC) coefficient distribution accords with a Laplace probability density distribution described with a suitable parameter. And on this basis, this paper proposed a new double compression probability model of JPEG image to describe the change of DCT coefficients after the double compression, and combined the Bayes criterion to express the eigenvalues of the image blocks which have undergone the single and double JPEG compression. A threshold was set for the eigenvalues. Then the tampered region was automatically detected and extracted by using the threshold to classify the eigenvalues. The experimental results show that the method can detect and locate the tamped area effectively and it outperforms in terms of the detection result compared with the blind detection algorithm of composite images by measuring inconsistencies of JPEG blocking artifact and image forgery detection algorithm based on quantization table especially when the second compression factor is smaller than the first one.

Community mining is an important application in the field of Web information mining. Topic detection and tracking is an important application in the field of text information study. Currently these two technologies are studied separately. To better apply these two technologies to complicated social networks formed by Internet, this paper combined them for research, discovered the relationships of community and topic, created static and dynamic interaction models for community mining and topic detection and tracking, and designed algorithms to mine communities, detect topics and track communities.