Search Results - "Tan, Shunquan"

Automatic Steganographic Distortion Learning Using a Generative Adversarial Network by Tang, Weixuan, Tan, Shunquan, Li, Bin, Huang, Jiwu

“…Generative adversarial network has shown to effectively generate artificial samples indiscernible from their real counterparts with a united framework of two…”
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CNN-Based Adversarial Embedding for Image Steganography by Weixuan Tang, Bin Li, Shunquan Tan, Barni, Mauro, Jiwu Huang

“…Steganographic schemes are commonly designed in a way to preserve image statistics or steganalytic features. Since most of the state-of-the-art steganalytic…”
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Large-Scale JPEG Image Steganalysis Using Hybrid Deep-Learning Framework by Zeng, Jishen, Tan, Shunquan, Li, Bin, Huang, Jiwu

“…Adoption of deep learning in image steganalysis is still in its initial stage. In this paper, we propose a generic hybrid deep-learning framework for JPEG…”
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Automatic Detection of Object-Based Forgery in Advanced Video by Chen, Shengda, Tan, Shunquan, Li, Bin, Huang, Jiwu

“…Passive multimedia forensics has become an active topic in recent years. However, less attention has been paid to video forensics. Research on video forensics,…”
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A Strategy of Clustering Modification Directions in Spatial Image Steganography by Li, Bin, Wang, Ming, Li, Xiaolong, Tan, Shunquan, Huang, Jiwu

“…Most of the recently proposed steganographic schemes are based on minimizing an additive distortion function defined as the sum of embedding costs for…”
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ReST-Net: Diverse Activation Modules and Parallel Subnets-Based CNN for Spatial Image Steganalysis by Li, Bin, Wei, Weihang, Ferreira, Anselmo, Tan, Shunquan

“…Recent steganalytic schemes reveal embedding traces in a promising way by using convolutional neural networks (CNNs). However, further improvements, such as…”
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Investigation on Cost Assignment in Spatial Image Steganography by Li, Bin, Tan, Shunquan, Wang, Ming, Huang, Jiwu

“…Relating the embedding cost in a distortion function to statistical detectability is an open vital problem in modern steganography. In this paper, we take one…”
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New Steganalytic Features for Spatial Image Steganography Based on Derivative Filters and Threshold LBP Operator by Li, Bin, Li, Zhongpeng, Zhou, Shijun, Tan, Shunquan, Zhang, Xiaoling

“…The standard local binary pattern (LBP) operator shows its versatility in performing image classification-related tasks, including texture analysis, object…”
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Effect of High Dipole Moment Cation on Layered 2D Organic–Inorganic Halide Perovskite Solar Cells by Tan, Shunquan, Zhou, Ning, Chen, Yihua, Li, Liang, Liu, Guilin, Liu, Pengfei, Zhu, Cheng, Lu, Jiuzhou, Sun, Wentao, Chen, Qi, Zhou, Huanping

“…Layered 2D organic–inorganic hybrid perovskite is appearing as a rising star in the photovoltaic field, thanks to its superior moisture resistance by the…”
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Targeted Steganalysis of Edge Adaptive Image Steganography Based on LSB Matching Revisited Using B-Spline Fitting by Shunquan Tan, Bin Li

“…In this letter, the authors point out that the readjusting phase of edge adaptive image steganography based on LSB matching revisited introduces a pulse…”
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Pixel-Decimation-Assisted Steganalysis of Synchronize-Embedding-Changes Steganography by Tan, Shunquan, Zhang, Haojie, Li, Bin, Huang, Jiwu

“…This paper deals with the state-of-the-art synchronize-embedding-changes (SECs) steganography. We propose the pixel-decimation-assisted steganalytic feature…”
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Statistical Model of JPEG Noises and Its Application in Quantization Step Estimation by Li, Bin, Ng, Tian-Tsong, Li, Xiaolong, Tan, Shunquan, Huang, Jiwu

“…In this paper, we present a statistical analysis of JPEG noises, including the quantization noise and the rounding noise during a JPEG compression cycle. The…”
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Revealing the Trace of High-Quality JPEG Compression Through Quantization Noise Analysis by Bin Li, Tian-Tsong Ng, Xiaolong Li, Shunquan Tan, Jiwu Huang

“…To identify whether an image has been JPEG compressed is an important issue in forensic practice. The state-of-the-art methods fail to identify high-quality…”
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A Novel Steganography for Spatial Color Images Based on Pixel Vector Cost by Qin, Xinghong, Li, Bin, Tan, Shunquan, Zeng, Jishen

“…Steganography aims to conceal secret data into common media, and steganalysis takes the adversarial position by trying to reveal embedding traces. Most of the…”
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30% Enhancement of Efficiency in Layered 2D Perovskites Absorbers by Employing Homo‐Tandem Structures by Chen, Yihua, Tan, Shunquan, Zhou, Ning, Yang, Ning, Zhou, Wentao, Wu, Yiliang, Weber, Klaus, Chen, Qi, Zhou, Huanping

“…Layered two dimensional (layered 2D) organic–inorganic metal halide perovskites have attracted tremendous interest in photovoltaics due to its acceptable…”
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Targeted steganalysis of adaptive pixel-value differencing steganography by Shunquan Tan, Bin Li

“…The adaptive pixel-value differencing steganography proposed by Luo et al. is a state-of-the-art content-adaptive steganographic method which resists blind…”
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Identification of deep network generated images using disparities in color components by Li, Haodong, Li, Bin, Tan, Shunquan, Huang, Jiwu

“…•Disparities between real and GAN-generated images are analyzed in different color spaces.•Generated images are more distinguishable in residual domain of…”
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Stacked convolutional auto-encoders for steganalysis of digital images by Shunquan Tan, Bin Li

“…In this paper, we point out that SRM (Spatial-domain Rich Model), the most successful steganalysis framework of digital images possesses a similar architecture…”
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Exploration of Crystallization Kinetics in Quasi Two-Dimensional Perovskite and High Performance Solar Cells by Zhou, Ning, Shen, Yiheng, Li, Liang, Tan, Shunquan, Liu, Na, Zheng, Guanhaojie, Chen, Qi, Zhou, Huanping

“…Halide perovskites with reduced-dimensionality (e.g., quasi-2D, Q-2D) have promising stability while retaining their high performance as compared to their…”
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A semi-supervised deep learning approach for cropped image detection by Hussain, Israr, Tan, Shunquan, Huang, Jiwu

“…Deep learning algorithms have shown great performance in multimedia forensics applications using supervised learning on large-scale labeled datasets. However,…”
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