28 papers with code • 0 benchmarks • 0 datasets

Detect the usage of Steganography

Most implemented papers

SteganoGAN: High Capacity Image Steganography with GANs

DAI-Lab/SteganoGAN 12 Jan 2019

Image steganography is a procedure for hiding messages inside pictures.

Learning Rich Features for Image Manipulation Detection

LarryJiang134/Image_manipulation_detection CVPR 2018

Image manipulation detection is different from traditional semantic object detection because it pays more attention to tampering artifacts than to image content, which suggests that richer features need to be learned.

Deep residual network for steganalysis of digital images


Steganography detectors built as deep convolutional neural networks have firmly established themselves as superior to the previous detection paradigm - classifiers based on rich media models.

GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection

socialabubi/iFakeFaceDB 13 Nov 2019

The availability of large-scale facial databases, together with the remarkable progresses of deep learning technologies, in particular Generative Adversarial Networks (GANs), have led to the generation of extremely realistic fake facial content, raising obvious concerns about the potential for misuse.

Empty Cities: a Dynamic-Object-Invariant Space for Visual SLAM

bertabescos/EmptyCities_SLAM 15 Oct 2020

The first challenge is addressed by the use of a convolutional network that learns a multi-class semantic segmentation of the image.

Structural Design of Convolutional Neural Networks for Steganalysis

albblgb/Deep-Steganalysis journal 2016

Recent studies have indicated that the architectures of convolutional neural networks (CNNs) tailored for computer vision may not be best suited to image steganalysis.

Deep learning hierarchical representations for image steganalysis

albblgb/Deep-Steganalysis journal 2016

Nowadays, the prevailing detectors of steganographic communication in digital images mainly consist of three steps, i. e., residual computation, feature extraction, and binary classification.

Steganographic Generative Adversarial Networks

dvolkhonskiy/adversarial-steganography 16 Mar 2017

Steganography is collection of methods to hide secret information ("payload") within non-secret information "container").

RNN-SM: Fast Steganalysis of VoIP Streams Using Recurrent Neural Network


Experiments show that on full embedding rate samples, RNN-SM is of high detection accuracy, which remains over 90% even when the sample is as short as 0. 1 s, and is significantly higher than other state-of-the-art methods.

Yedrouj-Net: An efficient CNN for spatial steganalysis

yedmed/steganalysis_with_CNN_Yedroudj-Net 26 Feb 2018

For about 10 years, detecting the presence of a secret message hidden in an image was performed with an Ensemble Classifier trained with Rich features.