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Name: Wenkang Su
Type: User
Company: GuangZhou University
Bio: Lecturer, Guangzhou University
Location: Guangzhou, China
Name: Wenkang Su
Type: User
Company: GuangZhou University
Bio: Lecturer, Guangzhou University
Location: Guangzhou, China
Efficient JPEG Steganography Using Domain Transformation of Embedding Entropy. https://ieeexplore.ieee.org/document/8322237
Steganalysis of JPEG Images Using Rich Models. Copy from: http://dde.binghamton.edu/download/feature_extractors/
New Design Paradigm of Distortion Cost Function for Efficient JPEG Steganography;Published in: Signal Processing; https://doi.org/10.1016/j.sigpro.2021.108319
Low Complexity Features for JPEG Steganalysis Using Undecimated DCT. Copy form: http://dde.binghamton.edu/download/feature_extractors/
PyTorch DDPM implementation
supplementary of "DWW: Robust Deep Wavelet-domain Watermarking with Enhanced Frequency Mask"
Ensemble Classifiers for Steganalysis of Digital Media, copy from http://dde.binghamton.edu/download/ensemble/
Efficient Audio Steganography using Generalized Audio Intrinsic Energy with Micro-Amplitude Modification Suppression
Steganalysis of Adaptive JPEG Steganography Using 2D Gabor Filters. Copy form: http://dde.binghamton.edu/download/feature_extractors/
Implementation of GigaGAN, new SOTA GAN out of Adobe
Image Steganography with Symmetric Embedding using Gaussian Markov Random Field Model;Published in: IEEE Transactions on Circuits and Systems for Video Technology;https://ieeexplore.ieee.org/document/9112323
A New Distortion Function Design for JPEG Steganography Using the Generalized Uniform Embedding Strategy
Towards improving the security of image steganography via minimizing the spatial embedding impact; Published in: Digital Signal Processing; https://doi.org/10.1016/j.dsp.2022.103758
Unofficial implementation of Image Super-Resolution via Iterative Refinement by Pytorch
Selection-Channel-Aware Rich Model for Steganalysis of Digital Images. Copy from http://dde.binghamton.edu/download/feature_extractors/
Content-Adaptive Steganography by Minimizing Statistical Detectability. Copy from: http://dde.binghamton.edu/download/stego_algorithms/
Multivariate Gaussian model for designing additive distortion for steganography. Copy from: http://dde.binghamton.edu/download/stego_algorithms/
Phase-Aware Projection Model for Steganalysis of JPEG Images. Copy from: http://dde.binghamton.edu/download/feature_extractors/
Random Projections of Residuals for Digital Image Steganalysis. Copy from: http://dde.binghamton.edu/download/feature_extractors/
Steganalysis Features for Content-Adaptive JPEG Steganography. Copy from:http://dde.binghamton.edu/download/feature_extractors/
Rich models for steganalysis of digital images. Copy from: http://dde.binghamton.edu/download/feature_extractors/
Minimizing Additive DistortionFunctions With Non-Binary Embedding Operation in Steganography. Copy form: http://dde.binghamton.edu/download/syndrome/
The code and supplementary material for the paper: "StegaStyleGAN: Towards Generic and Practical Generative Image Steganography"
matlabcode: Using Statistical Image Model for JPEG Steganography: Uniform Embedding Revisited
Universal Distortion Function for Steganography in an Arbitrary Domain. Copy from: http://dde.binghamton.edu/download/stego_algorithms/
Datasets, Transforms and Models specific to Computer Vision
Designing Steganographic Distortion Using Directional Filters. Copy from:http://dde.binghamton.edu/download/stego_algorithms/
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