检索结果-内蒙古大学图书馆

Revealing the processing history of pitch-shifted voice using CNNs 10

学校读者我要写书评

暂无评论

Revealing the processing history of pitch-shifted voice usin...

10th IEEE International Workshop on information Forensics and security, WIFS 2018

作者： Wang, Lihua Liang, Huixin Lin, Xiaodan Kang, Xiangui Guangdong Key Lab of Information Security Technology School of Data and Computer Science Sun Yat-sen University Guangzhou510006 China

ISBN: (纸本)9781538665367

With the emergence of varieties of audio editing software, disguised voices can be generated easily and are able to spoof the automatic speaker verification system. In this work, we focus on the spoofed voice disguised by pitch shifting which is commonly used by popular audio editing software and design a forensic chain to reveal the processing history of pitch-shifted voice signals blindly based on convolutional neural networks (CNNs). Because tracing the disguising tool before recognizing the disguising factor from the spoofed speech helps much to recognize the disguising factor, the proposed framework enables the detection of the disguised voice and the utilized tools. To our knowledge, it is the first attempt, focusing on voice transformation without any prior knowledge of the original speech, to recognize the disguising tool and the disguising factor from the spoofed speech, which is more suitable in real-world applications. Experimental results show that the disguising factors can be recognized with accuracy higher than 90%, indicating a high possibility of restoring the original voice from its disguised counterpart. © 2018 IEEE.

关键词： Convolution

Statistical learning aided list decoding of semi-random block oriented convolutional codes

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Lin, Wenchao Ma, Xiao Cai, Suihua Wei, Baodian School of Data and Computer Science Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510006 China School of Electronics and Information Technology Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510006 China

In this paper, we propose a statistical learning aided list decoding algorithm, which integrates a serial list Viterbi algorithm (SLVA) with a soft check instead of the conventional cyclic redundancy check (CRC), for semi-random block oriented convolutional codes (SRBO-CCs). The basic idea is that, compared with an erroneous candidate codeword, the correct candidate codeword for the first sub-frame has less effect on the output of Viterbi algorithm (VA) for the second sub-frame. The threshold for testing the correctness of the candidate codeword is then determined by learning the statistical behavior of the introduced empirical divergence function (EDF). With statistical learning aided list decoding, the performance-complexity tradeoff and the performance-delay tradeoff can be achieved by adjusting the statistical threshold and extending the decoding window, respectively. To analyze the performance, a closed-form upper bound and a simulated lower bound are derived. Simulation results verify our analysis and show that: 1) The statistical learning aided list decoding outperforms the sequential decoding in high signal-to-noise ratio (SNR) region;2) under the constraint of equivalent decoding delay, the SRBO-CCs have comparable performance with the polar codes. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolutional codes

Privacy-preserving medical treatment system through nondeterministic finite automata

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Yang, Yang Deng, Robert H. Liu, Ximeng Wu, Yongdong Weng, Jian Zheng, Xianghan Rong, Chunming School of Information Systems Singapore Management University Singapore College of Mathematics and Computer Science Fuzhou University Fujian China State Key Laboratory of Integrated Services Networks Xidian University Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China School of Information Systems Singapore Management University Singapore Singapore Department of Computer Science Jinan University Guangdong China College of Mathematics and Computer Science Fuzhou University Fujian China Department of Electronic Engineering and Computer Science University of Stavanger Norway MingByte Technology Qingdao China

In this paper, we propose a privacy-preserving medical treatment system using nondeterministic finite automata (NFA), hereafter referred to as P-Med, designed for the remote medical environment. P-Med makes use of the nondeterministic transition characteristic of NFA to flexibly represent the medical model, which includes illness states, treatment methods and state transitions caused by exerting different treatment methods. A medical model is encrypted and outsourced to the cloud to deliver telemedicine services. Using P-Med, patient-centric diagnosis and treatment can be made on-the-fly while protecting the confidentiality of a patient's illness states and treatment recommendation results. Moreover, a new privacy-preserving NFA evaluation method is given in P-Med to get a confidential match result for the evaluation of an encrypted NFA and an encrypted data set, which avoids the cumbersome inner state transition determination. We demonstrate that P-Med realizes treatment procedure recommendation without privacy leakage to unauthorized parties. We conduct extensive experiments and analyses to evaluate efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Diseases

Free ride on LDPC coded transmission

学校读者我要写书评

暂无评论

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

学校读者我要写书评

暂无评论

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

Syntax-enhanced Pre-trained model

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Xu, Zenan Guo, Daya Tang, Duyu Su, Qinliang Shou, Linjun Gong, Ming Zhong, Wanjun Quan, Xiaojun Jiang, Daxin Duan, Nan School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Microsoft Research Asia Beijing China Microsoft Search Technology Center Asia Beijing China Guangdong Key Laboratory Big Data Analysis and Processing Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Ministry of Education China

We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage, so that they suffer from discrepancy between the two stages. Such a problem would lead to the necessity of having human-annotated syntactic information, which limits the application of existing methods to broader scenarios. To address this, we present a model that utilizes the syntax of text in both pre-training and fine-tuning stages. Our model is based on Transformer with a syntax-aware attention layer that considers the dependency tree of the text. We further introduce a new pre-training task of predicting the syntactic distance among tokens in the dependency tree. We evaluate the model on three downstream tasks, including relation classification, entity typing, and question answering. Results show that our model achieves state-of-the-art performance on six public benchmark datasets. We have two major findings. First, we demonstrate that infusing automatically produced syntax of text improves pre-trained models. Second, global syntactic distances among tokens bring larger performance gains compared to local head relations between contiguous tokens. Copyright © 2020, The Authors. All rights reserved.

关键词： Syntactics

Fair electronic voting via bitcoin deposits 5th

学校读者我要写书评

暂无评论

Fair electronic voting via bitcoin deposits

5th International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2017

作者： Wu, Xijuan Wei, Baodian Tian, Haibo Du, Yusong Ma, Xiao School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China Chongqing Key Lab of Computer Network and Communication Technology Chongqing China

ISBN: (纸本)9789811308956

Bitcoin is the most popular decentralized digital currency now in use. Block chain is the basic technology of Bitcoin, providing a trustable ledger that can be publicly verified. Research on distributed applications based on block chain has become a new trend. We propose an electronic voting scheme based on block chain and prime numbers, which can support voting situations for multiple candidates. We design protocols for the Bitcoin voting situation, in which there are n voters and k candidates. Each voter will vote for one candidate. The proposed protocols could guarantee that the candidate who gets the majority voting wins the game and no individual voting information is disclosed. Due to the nature of the block chain, the voting results could not be tampered. It is transparent since the block chain is open to the public for verification. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Deposits

Depthwise Separable Convolutional Neural Network for Image Forensics

学校读者我要写书评

暂无评论

Depthwise Separable Convolutional Neural Network for Image F...

IEEE Visual Communications and Image Processing Conference

作者： Yifang Chen Feng Peng Xiangui Kang Z. Jane Wang Guangdong Key Lab of Information Security School of Data and Computer Science Sun Yat-sen University China ECE Dept. University of British Colombia Vancouver Canada

General-purpose forensics on small image patches appears to be feasible and important, but in fact poses a challenge due to insufficient statistics. Furthermore, there is a need to develop a forensic approach that can automatically learn effective and robust features related to image forensics with high parameter efficiency. In this paper, we propose a depthwise separable convolutional neural network (CNN) for the simultaneous detection of eleven types of image manipulations in image patches. Different from the previous CNNs based on standard convolution, depthwise separable convolution is introduced in the proposed CNN to adaptively extract forensics-related features from image patches with better parameter efficiency. When compared with four state-of-the-art methods, experiments demonstrate that the proposed CNN architecture can achieve better performance, e.g., the improvement in terms of accuracy in the detection of 32 × 32 images is up to 7.33%. It also achieves significantly better overall performance for different databases and better robustness against JPEG compression.

关键词：

Multi-view graph learning by joint modeling of consistency and inconsistency

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Liang, Youwei Huang, Dong Wang, Chang-Dong Yu, Philip S. College of Mathematics and Informatics South China Agricultural University Guangzhou China Tencent Shenzhen China College of Mathematics and Informatics South China Agricultural University Guangzhou China Pazhou Lab Guangzhou China School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

—Graph learning has emerged as a promising technique for multi-view clustering due to its ability to learn a unified and robust graph from multiple views. However, existing graph learning methods mostly focus on the multi-view consistency issue, yet often neglect the inconsistency between views, which makes them vulnerable to possibly low-quality or noisy datasets. To overcome this limitation, we propose a new multi-view graph learning framework, which for the first time simultaneously and explicitly models multi-view consistency and inconsistency in a unified objective function, through which the consistent and inconsistent parts of each single-view graph as well as the unified graph that fuses the consistent parts can be iteratively learned. Though optimizing the objective function is NP-hard, we design a highly efficient optimization algorithm that can obtain an approximate solution with linear time complexity in the number of edges in the unified graph. Furthermore, our multi-view graph learning approach can be applied to both similarity graphs and dissimilarity graphs, which lead to two graph fusion-based variants in our framework. Experiments on twelve multi-view datasets have demonstrated the robustness and efficiency of the proposed approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Optimization