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Notice of Removal: An improved spread transform dither modulation for robust and secure watermarking

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Notice of Removal: An improved spread transform dither modul...

IEEE International Conference on Image Processing

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Community Detection in Complicated Network Based on the Multi-view Weighted Signed Permanence

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Community Detection in Complicated Network Based on the Mult...

IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Juan-Hui Li Pei-Zhen Li Chang-Dong Wang Jian-Huang Lai Guangdong Key Laboratory of Information Security Technology Guangzhou China School of Data and Computer Science Sun Yat-sen University Guangzhou China

ISBN: (纸本)9781509032068

This paper addresses the community detection problem in multi-view weighted signed network. Although some methods have been developed to detect communities in multi-view network, they are mostly designed for the unweighted unsigned case. There is still a lack of methods for multi-view weighted signed network. Since an increasing number of multi-view weighted signed networks are being generated in some applications, there is a necessity to develop a community detection approach to reveal the complicated structure of such networks. In this paper, we extend the single-view permanence model to the multi-view weighted signed case, which is a node-level model by considering four factors based on the influence of the nodes, including internal connections, total connections, maximum external connections and internal clustering coefficient. Extensive experiments are conducted on some networks to evaluate the performance of our proposed approach.

关键词： Couplings Color Weight measurement Adaptation models Partitioning algorithms computer science

Adversarial Neural Machine Translation

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arXiv 2017年

作者： Wu, Lijun Xia, Yingce Zhao, Li Tian, Fei Qin, Tao Lai, Jianhuang Liu, Tie-Yan School of Data and Computer Science Sun Yat-sen University University of Science and Technology of China Microsoft Research Asia Guangdong Key Laboratory of Information Security Technology

In this paper, we study a new learning paradigm for Neural Machine Translation \(NMT\). Instead of maximizing the likelihood of the human translation as in previous works, we minimize the distinction between human translation and the translation given by an NMT model. To achieve this goal, inspired by the recent success of Generative Adversarial Networks \(GANs\), we employ an adversarial training architecture and name it as Adversarial-NMT. In Adversarial-NMT, the training of the NMT model is assisted by an adversary, which is an elab.rately designed Convolutional Neural Network \(CNN\). The goal of the adversary is to differentiate the translation result generated by the NMT model from that by human. The goal of the NMT model is to produce high quality translations so as to cheat the adversary. A policy gradient method is leveraged to co-train the NMT model and the adversary. Experimental results on English!French and German!English translation tasks show that Adversarial-NMT can achieve significantly better translation quality than several strong baselines. Copyright © 2017, The Authors. All rights reserved.

关键词： Neural machine translation

PEDESTRIAN DETECTION VIA A LEG-DRIVEN PHYSIOLOGY FRAMEWORK

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PEDESTRIAN DETECTION VIA A LEG-DRIVEN PHYSIOLOGY FRAMEWORK

IEEE International Conference on Image Processing

作者： Gongbo Liang Qi Li Xiangui Kang Western Kentucky University Department of Computer Science Ogden College of Science & Engineering Guangdong Key Lab of Information Security Sun Yat-Sen University School of Data and Computer Science

ISBN: (纸本)9781467399623

In this paper, we propose a leg-driven physiology framework for pedestrian detection. The framework is introduced to reduce the search space of candidate regions of pedestrians. Given a set of vertical line segments, we can generate a space of rectangular candidate regions, based on a model of body proportions. The proposed framework can be either integrated with or without learning-based pedestrian detection methods to validate the candidate regions. A symmetry constraint is then applied to validate each candidate region to decrease the false positive rate. The experiment demonstrates the promising results of the proposed method by comparing it with Dalal & Triggs method. For example, rectangular regions detected by the proposed method has much similar area to the ground truth than regions detected by Dalal & Triggs method.

关键词： Pedestrian detection Leg Line segment Bounding box bounding boxes Line sections Leg Pedestrians Physiology candidate species vertical line ground truth inspection methods

A cloud-based taxi trace mining framework for smart city

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A cloud-based taxi trace mining framework for smart city

作者： Liu, Jin Yu, Xiao Xu, Zheng Choo, Kim-Kwang Raymond Hong, Liang Cui, Xiaohui State Key Lab. of Software Engineering Computer School Wuhan University Wuhan China Third Research Institute of Ministry of Public Security Shanghai China Department of Information Systems and Cyber Security University of Texas at San Antonio San AntonioTX United States School of Information Technology and Mathematical Science University of South Australia AdelaideSA Australia School of Information Management Wuhan University Wuhan China International School of Software Wuhan University Wuhan China

As a well-known field of big data applications, smart city takes advantage of massive data analysis to achieve efficient management and sustainable development in the current worldwide urbanization process. An important problem in smart city is how to discover frequent trajectory sequence pattern and cluster trajectory. To solve this problem, this paper proposes a cloud-based taxi trajectory pattern mining and trajectory clustering framework for smart city. Our work mainly includes (1) preprocessing raw Global Positioning System trace by calling the Baidu API Geocoding;(2) proposing a distributed trajectory pattern mining (DTPM) algorithm based on Spark;and (3) proposing a distributed trajectory clustering (DTC) algorithm based on Spark. The proposed DTPM algorithm and DTC algorithm can overcome the high input/output overhead and communication overhead by adopting in-memory computation. In addition, the proposed DTPM algorithm can avoid generating redundant local trajectory patterns to significantly improve the overall performance. The proposed DTC algorithm can enhance the performance of trajectory similarity computation by transforming the trajectory similarity calculation into AND and OR operators. Experimental results indicate that DTPM algorithm and DTC algorithm can significantly improve the overall performance and scalab.lity of trajectory pattern mining and trajectory clustering on massive taxi trace data. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Clustering algorithms

Free ride on LDPC coded transmission

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arXiv 2019年

作者： Cai, Suihua Zhao, Shancheng Ma, Xiao School of Electronics and Information Technology Sun Yat-sen University Guangzhou510006 China College of Information Science and Technology Jinan University Guangzhou510632 China Guangdong Key Laboratory of Data Security and Privacy Preserving Guangzhou Key Laboratory of Data Security and Privacy Preserving Guangzhou510632 China School of Data and Computer Science and the Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510275 China

In this paper, we formulate a new problem to cope with the transmission of extra bits over an existing coded transmission link (referred to as coded payload link) without any cost of extra transmission energy or extra bandwidth. This is possible since a gap to the channel capacity typically exists for a practical code. A new concept, termed as accessible capacity, is introduced to specify the maximum rate at which the superposition transmission of extra bits is reliable and has a negligible effect on the performance of the coded payload link. For a binary-input output-symmetric (BIOS) memoryless channel, the accessible capacity can be characterized as the difference between the channel capacity and the mutual information rate of the coded payload link, which can be numerically evaluated for very short payload codes. For a general payload code, we present a simple lower bound on the accessible capacity, given by the channel capacity minus the coding rate of the payload code. We then focus on the scenarios where low-density parity-check (LDPC) codes are implemented for the payload link. We propose to transmit extra bits by random superposition for encoding, and exhaustive search (with the aid of statistical learning) for decoding. We further propose, by establishing an auxiliary channel (called syndrome channel) induced from "zero-forcing" over the binary field, to transmit extra bits with structured codes such as repetition codes and first-order Reed-Muller (RM) codes. Numerical results show that up to 60 extra bits can be reliably transmitted along with a rate-1/2 LDPC code of length 8064. Copyright © 2019, The Authors. All rights reserved.

关键词： Forward error correction

Multiple-Operation Image Anti-Forensics with WGAN-GP Framework

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Multiple-Operation Image Anti-Forensics with WGAN-GP Framewo...

Asia-Pacific Signal and information Processing Association Annual Summit and Conference (APSIPA)

作者： Jianyuan Wu Zheng Wang Hui Zeng Xiangui Kang Guangdong Key Lab of Information Security School of Data and Computer science Sun Yat-sen University China School of Computer Science & Tech. Southwest University of Science and Tech. Mianyang China

ISBN: (数字)9781728132488

ISBN: (纸本)9781728132495

A challenging task in the field of multimedia security involves concealing or eliminating the traces left by a chain of multiple manipulating operations, i.e., multiple-operation anti-forensics in short. However, the existing anti-forensic works concentrate on one specific manipulation, referred as single-operation anti-forensics. In this work, we propose using the improved Wasserstein generative adversarial networks with gradient penalty (WGAN-GP) to model image anti-forensics as an image-to-image translation problem and obtain the optimized anti-forensic models of multiple-operation. The experimental results demonstrate that our multiple-operation anti-forensic scheme successfully deceives the state-of-the-art forensic algorithms without significantly degrading the quality of the image, and even enhancing quality in most cases. To our best knowledge, this is the first attempt to explore the problem of multiple-operation anti-forensics.

关键词： Forensics Generators Training Detectors Kernel Transform coding Image coding

An improved exact sampling algorithm for the standard normal distribution

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arXiv 2020年

作者： Du, Yusong Fan, Baoying Wei, Baodian School of Data and Computer Science Sun Yat-sen University Guangzhou510006 China Guangdong Key Laboratory of Information Security Technology Guangzhou510006 China

In 2016, Karney proposed an exact sampling algorithm for the standard normal distribution. In this paper, we study the computational complexity of this algorithm under the random deviate model. Specifically, Karney’s algorithm requires the access to an infinite sequence of independently and uniformly random deviates over the range (0, 1). We give an estimate of the expected number of uniform deviates used by this algorithm until outputting a sample value, and present an improved algorithm with lower uniform deviate consumption. The experimental results also shows that our improved algorithm has better performance than Karney’s *** Codes 65C10 Copyright © 2020, The Authors. All rights reserved.

关键词： Learning algorithms

Improved xception with dual attention mechanism and feature fusion for face forgery detection

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arXiv 2021年

作者： Lin, Hao Luo, Weiqi Wei, Kangkang Liu, Minglin Guangdong Key Lab. of Information Security Technology Sun Yat-Sen University Guangzhou China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China

With the rapid development of deep learning technology, more and more face forgeries by deepfake are widely spread on social media, causing serious social concern. Face forgery detection has become a research hotspot in recent years, and many related methods have been proposed until now. For those images with low quality and/or diverse sources, however, the detection performances of existing methods are still far from satisfactory. In this paper, we propose an improved Xception with dual attention mechanism and feature fusion for face forgery detection. Different from the middle flow in original Xception model, we try to catch different high-semantic features of face images using different levels of convolution, and introduce the convolutional block attention module and feature fusion to refine and reorganize those high-semantic features. In the exit flow, we employ the self-attention mechanism and depthwise separable convolution to learn the global information and local information of the fused features separately to improve the classification ability of the proposed model. Experimental results evaluated on three Deepfake datasets demonstrate that the proposed method outperforms Xception as well as other related methods both in effectiveness and generalization ability. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution