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Image Processing Operations Identification via Convolutional Neural Network

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arXiv 2017年

作者： Chen, Bolin Li, Haodong Luo, Weiqi School of Data and Computer Science Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510006 China College of Information Engineering Shenzhen University Shenzhen518052 China

—In recent years, image forensics has attracted more and more attention, and many forensic methods have been proposed for identifying image processing operations. Up to now, most existing methods are based on hand crafted features, and just one specific operation is considered in their methods. In many forensic scenarios, however, multiple classification for various image processing operations is more practical. Besides, it is difficult to obtain effective features by hand for some image processing operations. In this paper, therefore, we propose a new convolutional neural network (CNN) based method to adaptively learn discriminative features for identifying typical image processing operations. We carefully design the high pass filter bank to get the image residuals of the input image, the channel expansion layer to mix up the resulting residuals, the pooling layers, and the activation functions employed in our method. The extensive results show that the proposed method can outperform the currently best method based on hand crafted features and three related methods based on CNN for image steganalysis and/or forensics, achieving the state-of-the-art results. Furthermore, we provide more supplementary results to show the rationality and robustness of the proposed model. Copyright © 2017, The Authors. All rights reserved.

关键词： Convolution

Block Markov superposition transmission with pulse position modulation over free-space optical links

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Journal of Communications and information Networks 2017年第4期2卷 142-150页

作者： Jinshun Zhu Shancheng Zhao Xiao Ma School of Electronics and Information Technology Sun Yat-sen UniversityGuangzhou 510006China College of Information Science and Technology Jinan UniversityGuangzhou 510632China School of Data and Computer Science Sun Yat-sen UniversityGuangzhou 510006China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen UniversityGuangzhou 510006China

In FSO(Free-Space Optical)communications,performance of the communication systems is severely degraded by atmospheric ***(Pulse Position Modulation)is widely used in FSO communication systems owing to its high power *** this paper,we present a combination of the BMST(Block Markov Superposition Transmission)technique and the PPM scheme to improve the reliability of the transmission over FSO *** on analyzing an equivalent system,a lower bound on the bit-error-rate of the proposed scheme is *** simulations are performed which show that the BMST-PPM system performs well under a wide range of turbulence conditions and improves the performance of the basic *** results also show that,the performance of the system with the sliding-window detection/decoding algorithm matches well with the lower bound in the low-error-rate region.

关键词： BMST FSO gamma-gamma channel model iterative detection/decoding algorithm PPM

Anti-forensics of camera identification and the triangle test by improved fingerprint-copy attack

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arXiv 2017年

作者： Li, Haodong Luo, Weiqi Rao, Quanquan Huang, Jiwu School of Electronics and Information Technology Sun Yatsen University Guangzhou China School of Data and Computer Science Guangdong Key Laboratory of Information Security Technology Sun Yatsen University Guangzhou China College of Information Engineering Shenzhen University Shenzhen China

The fingerprint-copy attack aims to confuse camera identification based on sensor pattern noise. However, the triangle test shows that the forged images undergone fingerprint-copy attack would share a non-PRNU (Photo-response nonuniformity) component with every stolen image, and thus can detect fingerprint-copy attack. In this paper, we propose an improved fingerprint-copy attack scheme. Our main idea is to superimpose the estimated fingerprint into the target image dispersedly, via employing a block-wise method and using the stolen images randomly and partly. We also develop a practical method to determine the strength of the superimposed fingerprint based on objective image quality. In such a way, the impact of non-PRNU component on the triangle test is reduced, and our improved fingerprint-copy attack is difficultly detected. The experiments evaluated on 2,900 images from 4 cameras show that our scheme can effectively fool camera identification, and significantly degrade the performance of the triangle test simultaneously. Copyright © 2017, The Authors. All rights reserved.

关键词： Cameras

A SVM-based EEG signal analysis: An auxiliary therapy for tinnitus 1

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8th International Conference on Brain Inspired Cognitive Systems, BICS 2016

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

ISBN: (数字)9783319496856

ISBN: (纸本)9783319496849

Tinnitus is a kind of auditory disease characterized by an ongoing conscious perception of a sound in the absence of any external sound source. It is a common symptom for which no effective treatment exists. Though many non-invasive functional imaging modalities have been rapidly developed and applied to this field, yet, whether the EEG signal can be utilized to distinguish tinnitus patients from normal populations has not been investigated. In the present study, we perform a binary classification based on EEG signal to distinguish tinnitus patients from normal populations. In this study, 22 subjects are involved in the experiment with 15 of them being tinnitus patients and the others being normal controls. The collected EEG signals are preprocessed in frequency domain and well represented as features that depict each subject. Then the linear support vector machine is applied to classify the subjects. Satisfactory results have been achieved, where the accuracy of the classification could reach 90.91% in spite of the undeniable fact that the collected EEG signals contain noises. Accordingly, the present study reveals that the EEG signals can be utilized to distinguish tinnitus patients from normal populations, which could be regarded as an auxiliary therapy in tinnitus. © Springer International Publishing AG 2016.

关键词： Support vector machines

JPEG steganalysis based on DenseNet

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arXiv 2017年

作者： Yang, Jianhua Shi, Yun-Qing Wong, Edward K. Kang, Xiangui Guangdong Key Lab of Information Security School of Data and Computer Science Sun Yat-Sen University Guangzhou510006 China Department of ECE New Jersey Institute of Technology NewarkNJ07102 United States Department of Computer Science and Engineering New York University Tandon School of Engineering BrooklynNY11201 United States

Different from the conventional deep learning work based on an images content in computer vision, deep steganalysis is an art to detect the secret information embedded in an image via deep learning, pose challenge of detection weak information invisible hidden in a host image thus learning in a very low signal-to-noise (SNR) case. In this paper, we propose a 32- layer convolutional neural Networks (CNNs) in to improve the efficiency of preprocess and reuse the features by concatenating all features from the previous layers with the same featuremap size, thus improve the flow of information and gradient. The shared features and bottleneck layers further improve the feature propagation and reduce the CNN model parameters dramatically. Experimental results on the BOSSbase, BOWS2 and ImageNet datasets have showed that the proposed CNN architecture can improve the performance and enhance the robustness. To further boost the detection accuracy, an ensemble architecture called as CNN-SCA-GFR is proposed, CNN-SCAGFR is also the first work to combine the CNN architecture and conventional method in the JPEG domain. Experiments show that it can further lower detection errors. Compared with the state-of-the-art method XuNet [1] on BOSSbase, the proposed CNN-SCA-GFR architecture can reduce detection error rate by 5.67% for 0.1 bpnzAC and by 4.41% for 0.4 bpnzAC while the number of training parameters in CNN is only 17% of what used by XuNet. It also decreases the detection errors from the conventional method SCA-GFR by 7.89% for 0.1 bpnzAC and 8.06% for 0.4 bpnzAC, respectively. Copyright © 2017, The Authors. All rights reserved.

关键词： Steganography

Robust Depth-based Person Re-identification

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arXiv 2017年

作者： Wu, Ancong Zheng, Wei-Shi Lai, Jian-Huang School of Electronics and Information Technology Sun Yat-sen University Guangzhou China Collaborative Innovation Center of High Performance Computing National University of Defense Technology Changsha410073 China School of Data and Computer Science Sun Yat-sen University Guangzhou China Ministry of Education China Guangdong Province Key Laboratory of Information Security China

Person re-identification (re-id) aims to match people across non-overlapping camera views. So far the RGB-based appearance is widely used in most existing works. However, when people appeared in extreme illumination or changed clothes, the RGB appearance-based re-id methods tended to fail. To overcome this problem, we propose to exploit depth information to provide more invariant body shape and skeleton information regardless of illumination and color change. More specifically, we exploit depth voxel covariance descriptor and further propose a locally rotation invariant depth shape descriptor called Eigen-depth feature to describe pedestrian body shape. We prove that the distance between any two covariance matrices on the Riemannian manifold is equivalent to the Euclidean distance between the corresponding Eigen-depth features. Furthermore, we propose a kernelized implicit feature transfer scheme to estimate Eigendepth feature implicitly from RGB image when depth information is not availab.e. We find that combining the estimated depth features with RGB-based appearance features can sometimes help to better reduce visual ambiguities of appearance features caused by illumination and similar clothes. The effectiveness of our models was validated on publicly availab.e depth pedestrian datasets as compared to related methods for person re-identification. Copyright © 2017, The Authors. All rights reserved.

关键词： Covariance matrix

Toward multidiversified ensemble clustering of high-dimensional data: From subspaces to metrics and beyond

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arXiv 2017年

作者： Huang, Dong Wang, Chang-Dong Lai, Jian-Huang Kwoh, Chee-Keong The College of Mathematics and Informatics South China Agricultural University Guangzhou China Pazhou Lab Guangzhou China The School of Computer Science and Engineering 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 The School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

The rapid emergence of high-dimensional data in various areas has brought new challenges to current ensemble clustering research. To deal with the curse of dimensionality, recently considerable efforts in ensemble clustering have been made by means of different subspace-based techniques. However, besides the emphasis on subspaces, rather limited attention has been paid to the potential diversity in similarity/dissimilarity metrics. It remains a surprisingly open problem in ensemble clustering how to create and aggregate a large population of diversified metrics, and furthermore, how to jointly investigate the multi-level diversity in the large populations of metrics, subspaces, and clusters in a unified framework. To tackle this problem, this paper proposes a novel multidiversified ensemble clustering approach. In particular, we create a large number of diversified metrics by randomizing a scaled exponential similarity kernel, which are then coupled with random subspaces to form a large set of metric-subspace pairs. Based on the similarity matrices derived from these metric-subspace pairs, an ensemble of diversified base clusterings can thereby be constructed. Further, an entropy-based criterion is utilized to explore the cluster-wise diversity in ensembles, based on which three specific ensemble clustering algorithms are presented by incorporating three types of consensus functions. Extensive experiments are conducted on 30 high-dimensional datasets, including 18 cancer gene expression datasets and 12 image/speech datasets, which demonstrate the superiority of our algorithms over the state-of-the-art. The source code is availab.e at https://***/huangdonghere/MDEC. Copyright © 2017, The Authors. All rights reserved.

关键词： Cluster analysis

Person re-identification by camera correlation aware feature augmentation

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arXiv 2017年

作者： Chen, Ying-Cong Zhu, Xiatian Zheng, Wei-Shi Lai, Jian-Huang School of Data and Computer Science Sun Yat-sen University Guangzhou510275 China Collaborative Innovation Center of High Performance Computing National University of Defense Technology Changsha410073 China Department of Computer Science and Engineering Chinese University of Hong Kong School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Key Laboratory of Machine Intelligence and Advanced Computing Sun Yatsen University Ministry of Education China Guangdong Province Key Laboratory of Information Security China

The challenge of person re-identification (re-id) is to match individual images of the same person captured by different non-overlapping camera views against significant and unknown cross-view feature distortion. While a large number of distance metric/subspace learning models have been developed for re-id, the cross-view transformations they learned are view-generic and thus potentially less effective in quantifying the feature distortion inherent to each camera view. Learning view-specific feature transformations for re-id (i.e., view-specific re-id), an under-studied approach, becomes an alternative resort for this problem. In this work, we formulate a novel view-specific person re-identification framework from the feature augmentation point of view, called Camera coRrelation Aware Feature augmenTation (CRAFT). Specifically, CRAFT performs cross-view adaptation by automatically measuring camera correlation from cross-view visual data distribution and adaptively conducting feature augmentation to transform the original features into a new adaptive space. Through our augmentation framework, view-generic learning algorithms can be readily generalized to learn and optimize view-specific sub-models whilst simultaneously modelling view-generic discrimination information. Therefore, our framework not only inherits the strength of view-generic model learning but also provides an effective way to take into account view specific characteristics. Our CRAFT framework can be extended to jointly learn view-specific feature transformations for person re-id across a large network with more than two cameras, a largely under-investigated but realistic re-id setting. Additionally, we present a domain-generic deep person appearance representation which is designed particularly to be towards view invariant for facilitating cross-view adaptation by CRAFT. We conducted extensively comparative experiments to validate the superiority and advantages of our proposed framework over state-of

关键词： Cameras

Notice of Removal: A secure watermarking technique without loss of robustness

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Notice of Removal: A secure watermarking technique without l...

IEEE International Conference on Image Processing

作者： Jian Cao Haodong Li Weiqi Luo Jiwu Huang Shenzhen Graduate School Harbin Institute of Technology Shenzhen China School of Data and Computer Science and Guangdong Key Lab. of Information Security and Technology Sun Yat-Sen University Guangzhou China College of Information Engineering and Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China

Removed.

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