Steganalysis
28 papers with code • 0 benchmarks • 0 datasets
Detect the usage of Steganography
Benchmarks
These leaderboards are used to track progress in Steganalysis
Most implemented papers
SteganoGAN: High Capacity Image Steganography with GANs
Image steganography is a procedure for hiding messages inside pictures.
Learning Rich Features for Image Manipulation Detection
LarryJiang134/Image_manipulation_detection
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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
albblgb/Deep-Steganalysis
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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2018
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
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
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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
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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
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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
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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
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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.
