Image Steganography
18 papers with code • 0 benchmarks • 0 datasets
Image Steganography is the main content of information hiding. The sender conceal a secret message into a cover image, then get the container image called stego, and finish the secret message’s transmission on the public channel by transferring the stego image. Then the receiver part of the transmission can reveal the secret message out. Steganalysis is an attack to the steganography algorithm. The listener on the public channel intercept the image and analyze whether the image contains secret information.
Source: Invisible Steganography via Generative Adversarial Networks
Benchmarks
These leaderboards are used to track progress in Image Steganography
Most implemented papers
SteganoGAN: High Capacity Image Steganography with GANs
Image steganography is a procedure for hiding messages inside pictures.
End-to-end Trained CNN Encode-Decoder Networks for Image Steganography
All the existing image steganography methods use manually crafted features to hide binary payloads into cover images.
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.
Multi-Image Steganography Using Deep Neural Networks
JapsimarSinghWahi/DeepSteganography
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Steganography is the science of hiding a secret message within an ordinary public message.
Invisible Steganography via Generative Adversarial Networks
Neykah/isgan
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Nowadays, there are plenty of works introducing convolutional neural networks (CNNs) to the steganalysis and exceeding conventional steganalysis algorithms.
BASN -- Learning Steganography with Binary Attention Mechanism
adamcavendish/BASN-Learning-Steganography-with-Binary-Attention-Mechanism
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Secret information sharing through image carrier has aroused much research attention in recent years with images' growing domination on the Internet and mobile applications.
VStegNET: Video Steganography Networkusing Spatio-Temporal features andMicro-Bottleneck
aayushm107/VStegNET
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? 2019
Traditional image steganography techniques hide the secret image intohigh-frequency regions of the cover images.
Destruction of Image Steganography using Generative Adversarial Networks
Uranium-Deng/Steganalysis-StegoRemoval
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Digital image steganalysis, or the detection of image steganography, has been studied in depth for years and is driven by Advanced Persistent Threat (APT) groups', such as APT37 Reaper, utilization of steganographic techniques to transmit additional malware to perform further post-exploitation activity on a compromised host.
An Automatic Cost Learning Framework for Image Steganography Using Deep Reinforcement Learning
a-pig-akab/SPAR-RL_porject
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journal 2020
In SPAR-RL, an agent utilizes a policy network which decomposes the embedding process into pixel-wise actions and aims at maximizing the total rewards from a simulated steganalytic environment, while the environment employs an environment network for pixel-wise reward assignment.
HiNet: Deep Image Hiding by Invertible Network
tomtomtommi/hinet
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ICCV 2021
Capacity, invisibility and security are three primary challenges in image hiding task.
