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1,488 results on '"Zhang, Xinpeng"'

201. Style Transferring Based Data Hiding for Color Images

Author

Puyang, Yi, Qian, Zhenxing, Yin, Zhaoxia, Zhang, Xinpeng, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Sun, Xingming, editor, Pan, Zhaoqing, editor, and Bertino, Elisa, editor

Published
2018
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202. Steganography by Constructing Marbling Texture

Author

Qian, Zhenxing, Pan, Lin, Li, Sheng, Zhang, Xinpeng, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Sun, Xingming, editor, Pan, Zhaoqing, editor, and Bertino, Elisa, editor

Published
2018
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203. Reversible Data Embedding and Scrambling Method Based on JPEG Images

Author

Puyang, Yi, Yin, Zhaoxia, Zhang, Xinpeng, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Sun, Xingming, editor, Pan, Zhaoqing, editor, and Bertino, Elisa, editor

Published
2018
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210. Robust Degraded Face Recognition Using Enhanced Local Frequency Descriptor and Multi-scale Competition

Author

Sun, Guangling, Li, Guoqing, and Zhang, Xinpeng

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recognizing degraded faces from low resolution and blurred images are common yet challenging task. Local Frequency Descriptor (LFD) has been proved to be effective for this task yet it is extracted from a spatial neighborhood of a pixel of a frequency plane independently regardless of correlations between frequencies. In addition, it uses a fixed window size named single scale of short-term Frequency transform (STFT). To explore the frequency correlations and preserve low resolution and blur insensitive simultaneously, we propose Enhanced LFD in which information in space and frequency is jointly utilized so as to be more descriptive and discriminative than LFD. The multi-scale competition strategy that extracts multiple descriptors corresponding to multiple window sizes of STFT and take one corresponding to maximum confidence as the final recognition result. The experiments conducted on Yale and FERET databases demonstrate that promising results have been achieved by the proposed Enhanced LFD and multi-scale competition strategy., Comment: 7 pages,7 figures

Published
2012

211. Adaptive 3D Mesh Steganography Based on Feature-Preserving Distortion

Author

Zhang, Yushu, Zhu, Jiahao, Xue, Mingfu, Zhang, Xinpeng, and Cao, Xiaochun

Abstract

Current 3D mesh steganography algorithms relying on geometric modification are prone to detection by steganalyzers. In traditional steganography, adaptive steganography has proven to be an efficient means of enhancing steganography security. Taking inspiration from this, we propose a highly adaptive embedding algorithm, guided by the principle of minimizing a carefully crafted distortion through efficient steganography codes. Specifically, we tailor a payload-limited embedding optimization problem for 3D settings and devise a feature-preserving distortion (FPD) to measure the impact of message embedding. The distortion takes on an additive form and is defined as a weighted difference of the effective steganalytic subfeatures utilized by the current 3D steganalyzers. With practicality in mind, we refine the distortion to enhance robustness and computational efficiency. By minimizing the FPD, our algorithm can preserve mesh features to a considerable extent, including steganalytic and geometric features, while achieving a high embedding capacity. During the practical embedding phase, we employ the $Q$Q-layered syndrome trellis code (STC). However, calculating the bit modification probability (BMP) for each layer of the $Q$Q-layered STC, given the variation of $Q$Q, can be cumbersome. To address this issue, we design a universal and automatic approach for the BMP calculation. The experimental results demonstrate that our algorithm achieves state-of-the-art performance in countering 3D steganalysis.

Published
2024
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213. Stealthy Backdoor Attacks On Deep Point Cloud Recognization Networks.

Author

Feng, Le, Qian, Zhenxing, Zhang, Xinpeng, and Li, Sheng

Subjects
ARTIFICIAL neural networks, POINT cloud, PATTERN recognition systems, IMAGE segmentation, INTERPOLATION
Abstract

Deep neural networks are vulnerable to backdoor attacks. Previous backdoor attacks have mainly focused on images. Unlike images composed of regular pixels, 3D point clouds are composed of irregular three-dimensional XYZ coordinates, which are widely used in areas such as autonomous driving and 3D measurement. As many deep neural networks have been developed for processing 3D point clouds, these networks also face the risk of backdoor attacks. Nevertheless, backdoor attacks on 3D point clouds have rarely been investigated. This paper proposes a stealthy backdoor attack on point clouds in the physical world, aiming to generate trainable non-rigid deformations as backdoor patterns. Instead of directly adding backdoor patterns onto the point clouds, we deform the 3D space of the point clouds to a new space, ensuring that all point clouds have the same backdoor deformation. We use point cloud alignment to overcome the inconsistency of backdoor deformation caused by shifting and scaling in the physical world. We also propose a physical transformation layer to combat the physical transformations. Additionally, we propose mask contrast learning to eliminate pseudo backdoor patterns to make the network's backdoor property stealthier. Extensive experiments indicate that the proposed method can achieve better attack success rates and stealthiness. [ABSTRACT FROM AUTHOR]

Published
2024
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214. Sparse Backdoor Attack Against Neural Networks.

Author

Zhong, Nan, Qian, Zhenxing, and Zhang, Xinpeng

Subjects
ARTIFICIAL neural networks, ARTIFICIAL intelligence, DEEP learning, OBJECT recognition algorithms, IMAGE segmentation
Abstract

Recent studies show that neural networks are vulnerable to backdoor attacks, in which compromised networks behave normally for clean inputs but make mistakes when a pre-defined trigger appears. Although prior studies have designed various invisible triggers to avoid causing visual anomalies, they cannot evade some trigger detectors. In this paper, we consider the stealthiness of backdoor attacks from input space and feature representation space. We propose a novel backdoor attack named sparse backdoor attack, and investigate the minimum required trigger to induce the well-trained networks to make incorrect results. A U-net-based generator is employed to create triggers for each clean image. Considering the stealthiness of the trigger, we restrict the elements of the trigger between −1 and 1. In the aspect of the feature representation domain, we adopt an entanglement cost function to minimize the gap between feature representations of benign and malicious inputs. The inseparability of benign and malicious feature representations contributes to the stealthiness of our attack against various model diagnosis-based defences. We validate the effectiveness and generalization of our method by conducting extensive experiments on multiple datasets and networks. [ABSTRACT FROM AUTHOR]

Published
2024
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236. Robust Steganography Using Texture Synthesis

Author

Qian, Zhenxing, Zhou, Hang, Zhang, Weiming, Zhang, Xinpeng, Howlett, Robert James, Series editor, Jain, Lakhmi C., Series editor, Pan, Jeng-Shyang, editor, Tsai, Pei-Wei, editor, and Huang, Hsiang-Cheh, editor

Published
2017
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237. Distortion Function for Spatial Image Steganography Based on the Polarity of Embedding Change

Author

Wang, Zichi, Lv, Jinpeng, Wei, Qingde, Zhang, Xinpeng, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Shi, Yun Qing, editor, Kim, Hyoung Joong, editor, Perez-Gonzalez, Fernando, editor, and Liu, Feng, editor

Published
2017
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238. Reversible Data Hiding in Encrypted AMBTC Compressed Images

Author

Niu, Xuejing, Yin, Zhaoxia, Zhang, Xinpeng, Tang, Jin, Luo, Bin, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Shi, Yun Qing, editor, Kim, Hyoung Joong, editor, Perez-Gonzalez, Fernando, editor, and Liu, Feng, editor

Published
2017
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