22 citations · 22 across the 2 of their papers we have counts for
7 papers
MCTSteg: A Monte Carlo Tree Search-based Reinforcement Learning Framework for Universal Non-additive Steganography
Xianbo Mo, Shunquan Tan, Bin Li +1
Recent research has shown that non-additive image steganographic frameworks effectively improve security performance through adjusting distortion distribution. However, as far as w…
Image Steganography based on Iteratively Adversarial Samples of A Synchronized-directions Sub-image
Xinghong Qin, Shunquan Tan, Bin Li +2
Nowadays a steganography has to face challenges of both feature based staganalysis and convolutional neural network (CNN) based steganalysis. In this paper, we present a novel steg…
CALPA-NET: Channel-pruning-assisted Deep Residual Network for Steganalysis of Digital Images
Shunquan Tan, Weilong Wu, Zilong Shao +3
Over the past few years, detection performance improvements of deep-learning based steganalyzers have been usually achieved through structure expansion. However, excessive expanded…
Identification of Deep Network Generated Images Using Disparities in Color Components
Haodong Li, Bin Li, Shunquan Tan +1
With the powerful deep network architectures, such as generative adversarial networks, one can easily generate photorealistic images. Although the generated images are not dedicate…
CNN Based Adversarial Embedding with Minimum Alteration for Image Steganography
Weixuan Tang, Bin Li, Shunquan Tan +2
Historically, steganographic schemes were designed in a way to preserve image statistics or steganalytic features. Since most of the state-of-the-art steganalytic methods employ a…
WISERNet: Wider Separate-then-reunion Network for Steganalysis of Color Images
Jishen Zeng, Shunquan Tan, Guangqing Liu +2
Until recently, deep steganalyzers in spatial domain have been all designed for gray-scale images. In this paper, we propose WISERNet (the wider separate-then-reunion network) for…