| Automatic steganographic distortion learning using a generative adversarial network W Tang, S Tan, B Li, J Huang IEEE Signal Processing Letters 24 (10), 1547-1551, 2017 | 404 | 2017 |
| Identification of deep network generated images using disparities in color components H Li, B Li, S Tan, J Huang Signal Processing 174, 107616, 2020 | 315 | 2020 |
| CNN-based adversarial embedding for image steganography W Tang, B Li, S Tan, M Barni, J Huang IEEE Transactions on Information Forensics and Security 14 (8), 2074-2087, 2019 | 305 | 2019 |
| Stacked convolutional auto-encoders for steganalysis of digital images S Tan, B Li Signal and information processing association annual summit and conference …, 2014 | 287 | 2014 |
| A strategy of clustering modification directions in spatial image steganography B Li, M Wang, X Li, S Tan, J Huang IEEE Transactions on Information Forensics and Security 10 (9), 1905-1917, 2015 | 280 | 2015 |
| Large-scale JPEG image steganalysis using hybrid deep-learning framework J Zeng, S Tan, B Li, J Huang IEEE Transactions on Information Forensics and Security 13 (5), 1200-1214, 2017 | 260 | 2017 |
| Investigation on cost assignment in spatial image steganography B Li, S Tan, M Wang, J Huang IEEE Transactions on Information Forensics and Security 9 (8), 1264-1277, 2014 | 190 | 2014 |
| Automatic detection of object-based forgery in advanced video S Chen, S Tan, B Li, J Huang IEEE Transactions on Circuits and Systems for Video Technology 26 (11), 2138 …, 2015 | 168 | 2015 |
| ReST-Net: Diverse activation modules and parallel subnets-based CNN for spatial image steganalysis B Li, W Wei, A Ferreira, S Tan IEEE Signal Processing Letters 25 (5), 650-654, 2018 | 144 | 2018 |
| Image tampering localization using a dense fully convolutional network P Zhuang, H Li, S Tan, B Li, J Huang IEEE Transactions on Information Forensics and Security 16, 2986-2999, 2021 | 126 | 2021 |
| New steganalytic features for spatial image steganography based on derivative filters and threshold LBP operator B Li, Z Li, S Zhou, S Tan, X Zhang IEEE Transactions on Information Forensics and Security 13 (5), 1242-1257, 2017 | 92 | 2017 |
| Self-adversarial training incorporating forgery attention for image forgery localization L Zhuo, S Tan, B Li, J Huang IEEE Transactions on Information Forensics and Security 17, 819-834, 2022 | 85 | 2022 |
| WISERNet: Wider separate-then-reunion network for steganalysis of color images J Zeng, S Tan, G Liu, B Li, J Huang IEEE Transactions on Information Forensics and Security 14 (10), 2735-2748, 2019 | 78 | 2019 |
| Revealing the trace of high-quality JPEG compression through quantization noise analysis B Li, TT Ng, X Li, S Tan, J Huang IEEE Transactions on Information Forensics and Security 10 (3), 558-573, 2015 | 72 | 2015 |
| Targeted steganalysis of edge adaptive image steganography based on LSB matching revisited using B-spline fitting S Tan, B Li IEEE Signal Processing Letters 19 (6), 336-339, 2012 | 65 | 2012 |
| A survey on deep convolutional neural networks for image steganography and steganalysis I Hussain, J Zeng, X Qin, S Tan KSII Transactions on Internet and Information Systems (TIIS) 14 (3), 1228-1248, 2020 | 59 | 2020 |
| CALPA-NET: Channel-pruning-assisted deep residual network for steganalysis of digital images S Tan, W Wu, Z Shao, Q Li, B Li, J Huang IEEE Transactions on Information Forensics and Security 16, 131-146, 2020 | 54 | 2020 |
| Learning features of intra-consistency and inter-diversity: Keys toward generalizable deepfake detection H Chen, Y Lin, B Li, S Tan IEEE Transactions on Circuits and Systems for Video Technology 33 (3), 1468-1480, 2022 | 49 | 2022 |
| Statistical model of JPEG noises and its application in quantization step estimation B Li, TT Ng, X Li, S Tan, J Huang IEEE Transactions on Image Processing 24 (5), 1471-1484, 2015 | 46 | 2015 |
| Pre-training via fitting deep neural network to rich-model features extraction procedure and its effect on deep learning for steganalysis J Zeng, S Tan, B Li, J Huang Electronic Imaging 29, 44-49, 2017 | 39 | 2017 |