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Bolin Chen
Researcher at Sun Yat-sen University
Publications - 13
Citations - 327
Bolin Chen is an academic researcher from Sun Yat-sen University. The author has contributed to research in topics: Steganalysis & Steganography. The author has an hindex of 6, co-authored 12 publications receiving 183 citations.
Papers
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Proceedings ArticleDOI
Fake Faces Identification via Convolutional Neural Network
Huaxiao Mo,Bolin Chen,Weiqi Luo +2 more
TL;DR: This paper proposes a Convolutional Neural Network (CNN) based method to identify fake face images generated by the current best method, and provides experimental evidences to show that the proposed method can achieve satisfactory results with an average accuracy over 99.4%.
Proceedings ArticleDOI
Fast and Effective Global Covariance Pooling Network for Image Steganalysis
TL;DR: Experimental results show that the proposed convolutional neural network for image steganalysis in spatial domain can outperform the current best one, while its training time is significantly reduced.
Journal ArticleDOI
Audio Steganography Based on Iterative Adversarial Attacks Against Convolutional Neural Networks
TL;DR: This work introduces a novel steganography method based on adversarial examples for digital audio in the time domain that significantly outperforms the existing nonadaptive and adaptive Steganography methods and achieves state-of-the-art results.
Proceedings ArticleDOI
Audio Steganalysis with Convolutional Neural Network
Bolin Chen,Weiqi Luo,Haodong Li +2 more
TL;DR: A novel CNN (convolutional neural networks) to detect audio steganography in the time domain is designed and extensive experimental results evaluated on 40,000 speech audio clips have shown the effectiveness of the proposed convolutional network.
Journal ArticleDOI
Image Processing Operations Identification via Convolutional Neural Network
Bolin Chen,Haodong Li,Weiqi Luo +2 more
TL;DR: Wang et al. as discussed by the authors proposed a new convolutional neural network (CNN) based method to adaptively learn discriminative features for identifying typical image processing operations, which 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.