Open Access

ARTICLE

Dunhong Yao^1,2,3,*, Yu Chen⁴
Journal of Information Hiding and Privacy Protection, Vol.2, No.2, pp. 59-65, 2020, DOI:10.32604/jihpp.2020.010223 - 11 November 2020
Abstract With the rapid development of the Internet, many enterprises have launched their network platforms. When users browse, search, and click the products of these platforms, most platforms will keep records of these network behaviors, these records are often heterogeneous, and it is called log data. To effectively to analyze and manage these heterogeneous log data, so that enterprises can grasp the behavior characteristics of their platform users in time, to realize targeted recommendation of users, increase the sales volume of enterprises’ products, and accelerate the development of enterprises. Firstly, we follow the process of big… More >

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Open Access

ARTICLE

Rongyu Chen, Lili Pan^*, Yan Zhou, Qianhui Lei
Journal of Information Hiding and Privacy Protection, Vol.2, No.2, pp. 67-76, 2020, DOI:10.32604/jihpp.2020.010472 - 11 November 2020
Abstract With the rapid development of information technology, the speed and efficiency of image retrieval are increasingly required in many fields, and a compelling image retrieval method is critical for the development of information. Feature extraction based on deep learning has become dominant in image retrieval due to their discrimination more complete, information more complementary and higher precision. However, the high-dimension deep features extracted by CNNs (convolutional neural networks) limits the retrieval efficiency and makes it difficult to satisfy the requirements of existing image retrieval. To solving this problem, the high-dimension feature reduction technology is… More >

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Open Access

ARTICLE

Yan Zhou, Lili Pan^*, Rongyu Chen, Weizhi Shao
Journal of Information Hiding and Privacy Protection, Vol.2, No.2, pp. 77-86, 2020, DOI:10.32604/jihpp.2020.010486 - 11 November 2020
Abstract In large-scale image retrieval, deep features extracted by Convolutional Neural Network (CNN) can effectively express more image information than those extracted by traditional manual methods. However, the deep feature dimensions obtained by Deep Convolutional Neural Network (DCNN) are too high and redundant, which leads to low retrieval efficiency. We propose a novel image retrieval method, which combines deep features selection with improved DCNN and hash transform based on high-dimension features reduction to gain lowdimension deep features and realizes efficient image retrieval. Firstly, the improved network is based on the existing deep model to build a… More >

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REVIEW

Xin Liu^*, Xiao Chen
Journal of Information Hiding and Privacy Protection, Vol.2, No.2, pp. 87-94, 2020, DOI:10.32604/jihpp.2020.09839 - 11 November 2020
Abstract In recent years, with the rapid growth of generative adversarial networks (GANs), a photo-realistic face can be easily generated from a random vector. Moreover, the faces generated by advanced GANs are very realistic. It is reasonable to acknowledge that even a well-trained viewer has difficulties to distinguish artificial from real faces. Therefore, detecting the face generated by GANs is a necessary work. This paper mainly introduces some methods to detect GAN-generated fake faces, and analyzes the advantages and disadvantages of these models based on the network structure and evaluation indexes, and the results obtained in More >

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Open Access

ARTICLE

Bingtao Hu^*, Jinwei Wang
Journal of Information Hiding and Privacy Protection, Vol.2, No.2, pp. 95-105, 2020, DOI:10.32604/jihpp.2020.010464 - 11 November 2020
Abstract With the development of computer graphics, realistic computer graphics (CG) have become more and more common in our field of vision. This rendered image is invisible to the naked eye. How to effectively identify CG and natural images (NI) has been become a new issue in the field of digital forensics. In recent years, a series of deep learning network frameworks have shown great advantages in the field of images, which provides a good choice for us to solve this problem. This paper aims to track the latest developments and applications of deep learning in More >

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