The rapid development of big data and cloud computing technologies greatly accelerate the spreading and utilization of images and videos. The copyright protection for images and videos is becoming increasingly serious. In this paper, we proposed the robust non-blind watermarking schemes in YCbCr color space based on channel coding. The source watermark image is encoded and singular value decomposed. Subsequently, the singular value matrixes are embedded into the Y, Cb, and Cr components of the host image after four-level discrete wavelet transform (DWT). The embedding factor for each component is calculated based on the just-noticeable distortion and the singular vectors of HL subband of DWT. The peak signal-to-noise ratio of the watermarked image and the normalized correlation coefficient of the extracted watermark are investigated. It is shown that the proposed channel coding-based schemes can achieve near exact watermark recovery against all kinds of attacks. Considering both robustness and transparency, the convolutional code-based additive embedding scheme is optimal, which can also achieve good performance for video watermarking after extension.

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The encrypted image retrieval in cloud computing is a key technology to realize the massive images of storage and management and images safety. In this paper, a novel feature extraction method for encrypted image retrieval is proposed. First, the improved Harris algorithm is used to extract the image features. Next, the Speeded-Up Robust Features algorithm and the Bag of Words model are applied to generate the feature vectors of each image. Then, Local Sensitive Hash algorithm is applied to construct the searchable index for the feature vectors. The chaotic encryption scheme is utilized to protect images and indexes security. Finally, secure similarity search is executed on the cloud server. The experimental results show that compared with the existing encryption retrieval schemes, the proposed retrieval scheme not only reduces the time consumption but also improves the image retrieval accuracy. INDEX TERMS Cloud computing, image retrieval, Harris corner detection, local sensitive hash.

Citrus is one of the most widely cultivated fruit in the world. However, citrus diseases are becoming more and more serious, which has caused substantial economic losses to citrus growers. With the rapid developments of mobile device, mobile services computing play an increasingly important role in our daily lives. How to develop an intelligent diagnosis system for citrus diseases based on mobile services computing and bridge the gap between citrus growers and plant diagnostic experts is worth studying. In this paper, we build an image dataset of six kinds of citrus diseases with the help of experts and realize an intelligent diagnosis system for citrus diseases by constructing the simplified densely connected convolutional networks (DenseNet). The system is realized using the WeChat applet in the mobile device, with which users can upload images and receive diagnostic results and comments. The experimental results show that the recognition accuracy of citrus diseases exceeds 88% and the predict time consumption has also been reduced by simplifying the structure of the DenseNet.

Determination of image authenticity usually requires the identification and localization of the manipulated regions of images. Hence, image manipulation detection has become one of the most important tasks in the field of multimedia forensics. Recently, Convolutional Neural Networks (CNNs) have achieved promising performance in image manipulation detection. However, it is hard for the existing CNN-based manipulation detection approaches to accurately identify and localize the manipulated regions that have undergone geometric transformations, since CNNs are limited by their inability to be geometrically invariant. To address this issue, we propose a geometric rectification-based neural network architecture for image manipulation detection. In this type of network architecture, following the detection of a set of potential manipulated regions (PMRs) using Region Proposal Network, the Spatial Transformer Network is employed to geometrically rectify the convolutional feature maps (CFMs) of these regions to obtain the geometrically rectified CFMs (GR-CFMs). Subsequently, the residual feature maps (RFMs) are computed to capture the characteristic inconsistency between the CFMs and GR-CFMs of each PMR. Finally, the computed RFMs are automatically integrated with the GR-CFMs by a designed attention module to determine whether each PMR is a Grant/Award Numbers: MOST 108-2221-E-259-009-MY2, 109-2221-E-259-010 manipulated region and to localize the manipulated part at the pixel-level. Extensive experiments on the public data set as well as on our challenging data set demonstrate that the proposed network architecture achieves desirable performance in identifying and localizing regions with common tampering artifacts, which involve geometric transformations.

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As more and more image data are stored in the encrypted form in the cloud computing environment, it has become an urgent problem that how to efficiently retrieve images on the encryption domain. Recently, Convolutional Neural Network (CNN) features have achieved promising performance in the field of image retrieval, but the high dimension of CNN features will cause low retrieval efficiency. Also, it is not suitable to directly apply them for image retrieval on the encryption domain. To solve the above issues, this paper proposes an improved CNN-based hashing method for encrypted image retrieval. First, the image size is increased and inputted into the CNN to improve the representation ability. Then, a lightweight module is introduced to replace a part of modules in the CNN to reduce the parameters and computational cost. Finally, a hash layer is added to generate a compact binary hash code. In the retrieval process, the hash code is used for encrypted image retrieval, which greatly improves the retrieval efficiency. The experimental results show that the scheme allows an effective and efficient retrieval of encrypted images.