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Digital image splicing detection based on Markov features in QDCT and QWT domain

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arXiv 2017年

作者： Wang, Ruxin Lu, Wei Xiang, Shijun Zhao, Xianfeng Wang, Jinwei School of Data and Computer Science Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510006 China College of Information Science and Technology Jinan University Guangzhou510632 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

Image splicing detection is of fundamental importance in digital forensics and therefore has attracted increasing attention recently. In this paper, a color image splicing detection approach is proposed based on Markov transition probability of quaternion component separation in quaternion discrete cosine transform (QDCT) domain and quaternion wavelet transform (QWT) domain. Firstly, Markov features of the intra-block and inter-block between block QDCT coefficients are obtained from the real part and three imaginary parts of QDCT coefficients respectively. Then, additional Markov features are extracted from luminance (Y) channel in quaternion wavelet transform domain to characterize the dependency of position among quaternion wavelet subband coefficients. Finally, ensemble classifier (EC) is exploited to classify the spliced and authentic color images. The experiment results demonstrate that the proposed approach can outperforms some state-of-the-art methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Discrete cosine transforms

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

Universal stego post-processing for enhancing image steganography

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arXiv 2019年

作者： Chen, Bolin Luo, Weiqi Zheng, Peijia Huang, Jiwu 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

It is well known that the designing or improving embedding cost becomes a key issue for current steganographic methods. Unlike existing works, we propose a novel framework to enhance the steganography security via post-processing on the embedding units (i.e., pixel values and DCT coefficients) of stego directly. In this paper, we firstly analyze the characteristics of STCs (Syndrome-Trellis Codes), and then design the rule for post-processing to ensure the correct extraction of hidden message. Since the steganography artifacts are typically reflected on image residuals, we try to reduce the residual distance between cover and the modified stego in order to enhance steganography security. To this end, we model the post-processing as a non-linear integer programming, and implement it via heuristic search. In addition, we carefully determine several important issues in the proposed post-processing, such as the candidate embedding units to be modified, the direction and amplitude of post-modification, the adaptive filters for getting residuals, and the distance measure of residuals. Extensive experimental results evaluated on both hand-crafted steganalytic features and deep learning based ones demonstrate that the proposed method can effectively enhance the security of most modern steganographic methods both in spatial and JPEG domains. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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

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通信与信息网络学报（英文） 2018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 University Guangzhou 510006 China College of Information Science and Technology Jinan University Guangzhou 510632 China School of Data and Computer Science Sun Yat-sen University Guangzhou 510006 China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou 510006 China

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

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

Batch face alignment using a low-rank Gan

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arXiv 2019年

作者： Huang, Jiabo Xie, Xiaohua Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University China Guangdong Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

This paper studies the problem of aligning a set of face images of the same individual into a normalized image while removing the outliers like partial occlusion, extreme facial expression as well as significant illumination variation. Our model seeks an optimal image domain transformation such that the matrix of misaligned images can be decomposed as the sum of a sparse matrix of noise and a rank-one matrix of aligned images. The image transformation is learned in an unsupervised manner, which means that ground-truth aligned images are unnecessary for our model. Specifically, we make use of the remarkable non-linear transforming ability of generative adversarial network(GAN) and guide it with low-rank generation as well as sparse noise constraint to achieve the face alignment. We verify the efficacy of the proposed model with extensive experiments on real-world face databases, demonstrating higher accuracy and efficiency than existing methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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

Block Compressed Sensing-Based Joint Source-Channel Coding for Wireless Image Transmission

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Block Compressed Sensing-Based Joint Source-Channel Coding f...

International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Leqi Zheng Tingting Zhu Xiao Ma School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

ISBN: (数字)9781728172361

ISBN: (纸本)9781728172378

This paper presents a new joint source-channel coding (JSCC) scheme for wireless image transmission, which combines block compressed sensing (BCS) with systematic convolutional low density generator matrix (SC-LDGM) codes. In the proposed scheme (referred to as JSCC with BCS and SC-LDGM), an original image is firstly compressed by BCS and then quantized by dead-zone quantizer into ternary symbols. The resulting quantized sequence is encoded directly by a ternary SC-LDGM code and modulated by ternary pulse amplitude modulation (3-PAM). This scheme has the advantages to avoid the use of entropy coding and to match the quantized symbols with ternary coded modulations. Simulation results show that, the proposed scheme outperforms the conventional unequal error protection (UEP) scheme about 6 dB in terms of the energy-to-noise ratio (ENR).

关键词： Image coding Image reconstruction Wireless communication Image communication Compressed sensing Wireless sensor networks Channel coding

Universal deep network for steganalysis of color image based on channel representation

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arXiv 2021年

作者： Wei, Kangkang Luo, Weiqi Tan, Shunquan Huang, Jiwu The Guangdong Key Lab of Information Security Technology School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Up to now, most existing steganalytic methods are designed for grayscale images, and they are not suitable for color images that are widely used in current social networks. In this paper, we design a universal color image steganalysis network (called UCNet) in spatial and JPEG domains. The proposed method includes preprocessing, convolutional, and classification modules. To preserve the steganographic artifacts in each color channel, in preprocessing module, we firstly separate the input image into three channels according to the corresponding embedding spaces (i.e. RGB for spatial steganography and YCbCr for JPEG steganography), and then extract the image residuals with 62 fixed high-pass filters, finally concatenate all truncated residuals for subsequent analysis rather than adding them together with normal convolution like existing CNN-based steganalyzers. To accelerate the network convergence and effectively reduce the number of parameters, in convolutional module, we carefully design three types of layers with different shortcut connections and group convolution structures to further learn high-level steganalytic features. In classification module, we employ a global average pooling and fully connected layer for classification. We conduct extensive experiments on ALASKA II to demonstrate that the proposed method can achieve state-of-the-art results compared with the modern CNN-based steganalyzers (e.g., SRNet and J-YeNet) in both spatial and JPEG domains, while keeping relatively few memory requirements and training time. Furthermore, we also provide necessary descriptions and many ablation experiments to verify the rationality of the network design. Copyright © 2021, The Authors. All rights reserved.

关键词： Steganography