Journal of Information Hiding and Privacy Protection

About the Journal

Journal of Information Hiding and Privacy Protectionfocuses on original papers addressing novel ideas, issues, theoretical analysis, implementation, experimental results, systems and applications in the field of Watermarking, Data Hiding, Multimedia Security, and Privacy Protection.

  • Feature-Enhanced RefineDet: Fast Detection of Small Objects
  • Abstract Object detection has been studied for many years. The convolutional neural network has made great progress in the accuracy and speed of object detection. However, due to the low resolution of small objects and the representation of fuzzy features, one of the challenges now is how to effectively detect small objects in images. Existing target detectors for small objects: one is to use high-resolution images as input, the other is to increase the depth of the CNN network, but these two methods will undoubtedly increase the cost of calculation and time-consuming. In this paper, based on the RefineDet network framework,… More
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  • XGBoost Algorithm under Differential Privacy Protection
  • Abstract Privacy protection is a hot research topic in information security field. An improved XGBoost algorithm is proposed to protect the privacy in classification tasks. By combining with differential privacy protection, the XGBoost can improve the classification accuracy while protecting privacy information. When using CART regression tree to build a single decision tree, noise is added according to Laplace mechanism. Compared with random forest algorithm, this algorithm can reduce computation cost and prevent overfitting to a certain extent. The experimental results show that the proposed algorithm is more effective than other traditional algorithms while protecting the privacy information in training data. More
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  • A Fast Detection Method of Network Crime Based on User Portrait
  • Abstract In order to quickly and accurately find the implementer of the network crime, based on the user portrait technology, a rapid detection method for users with abnormal behaviorsis proposed. This method needs to construct the abnormal behavior rule base on various kinds of abnormal behaviors in advance, and construct the user portrait including basic attribute tags, behavior attribute tags and abnormal behavior similarity tagsfor network users who have abnormal behaviors. When a network crime occurs, firstly get the corresponding tag values in all user portraits according to the category of the network crime. Then, use the Naive Bayesian method matching… More
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  • Survey on the Loss Function of Deep Learning in Face Recognition
  • Abstract With the continuous development of face recognition network, the selection of loss function plays an increasingly important role in improving accuracy. The loss function of face recognition network needs to minimize the intra-class distance while expanding the inter-class distance. So far, one of our mainstream loss function optimization methods is to add penalty terms, such as orthogonal loss, to further constrain the original loss function. The other is to optimize using the loss based on angular/cosine margin. The last is Triplet loss and a new type of joint optimization based on HST Loss and ACT Loss. In this paper, based… More
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  • A Broadcast Storm Detection and Treatment Method Based on Situational Awareness
  • Abstract At present, the research of blockchain is very popular, but the practical application of blockchain is very few. The main reason is that the concurrency of blockchain is not enough to support application scenarios. After that, applications such as Intervalue increase the concurrency of blockchain transactions. However, due to the problems of network bandwidth and algorithm performance, there is always a broadcast storm, which affects the normal use of nodes in the whole network. However, the emergence of broadcast storms needs to rely on the node itself, which may be very slow. Even if developers debug the corresponding code, they… More
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