Structural Design of Convolutional Neural Networks for Steganalysis
By Guanshuo Xu, Han Wu, Y. Shi. IEEE Signal Processing Letters, 2016
744 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/a92537472ff31aed71498e216cefb5a77e20c8de
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In Computer Vision and Pattern Recognition
- Luisa Verdoliva
- Matthew Christopher Stamm
- Bin Li
- Belhassen Bayar
- Gongshen Liu
- Feng Zhu
- Ru Zhang
- Jianyi Liu
Citations in the corpus
- Deep Learning Hierarchical Representations for Image Steganalysis (2017, methodology): “The authors extended their works later in [14] by employing the network in [13] as base learners for ensemble classifier and obtained the results that can rival the SRM.”
- Deep Residual Network for Steganalysis of Digital Images (2019, methodology): “[60], which we call in this paper J-XuNet to distinguish it from the network introduced in [62].”
- Automatic Steganographic Distortion Learning Using a Generative Adversarial Network (2017, methodology): “The discriminator D adopts Xu’s model [7].”
- Yedroudj-Net: An Efficient CNN for Spatial Steganalysis (2018, positive): “The relevance of this layer was observed in Xu-Net [5].”
- Constrained Convolutional Neural Networks: A New Approach Towards General Purpose Image Manipulation Detection (2018, methodology): “In these experiments, we used the same high-pass filter commonly employed in forensics and steganalysis [37], [38].”
- Depth-Wise Separable Convolutions and Multi-Level Pooling for an Efficient Spatial CNN-Based Steganalysis (2020, methodology): “For a fair comparison, we report the performance of Yedroudj-Net, Ye-Net, Xu-Net, SRNet and the Spatial Rich Model + the Ensemble Classifier (SRM + EC), against the embedding algorithm WOW and S-UNIWARD at payload 0.2 bpp and 0.4 bpp.”
- A New Payload Partition Strategy in Color Image Steganography (2020, methodology): “Researchers have begun to investigate the potential of CNN in image steganalysis [43], [44].”
- Deep residual learning for image steganalysis (2018, methodology): “[36] inserted an absolute layer behind the first convolutional layer.”