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
Latest papers with no code
Enhancing Steganographic Text Extraction: Evaluating the Impact of NLP Models on Accuracy and Semantic Coherence
This study discusses a new method combining image steganography technology with Natural Language Processing (NLP) large models, aimed at improving the accuracy and robustness of extracting steganographic text.
Noise-NeRF: Hide Information in Neural Radiance Fields using Trainable Noise
Neural radiance fields (NeRF) have been proposed as an innovative 3D representation method.
From Covert Hiding to Visual Editing: Robust Generative Video Steganography
Traditional video steganography methods are based on modifying the covert space for embedding, whereas we propose an innovative approach that embeds secret message within semantic feature for steganography during the video editing process.
Null Space Properties of Neural Networks with Applications to Image Steganography
Through experiments on image datasets such as MNIST, we show that we can use null space components to force the neural network to choose a selected hidden image class, even though the overall image can be made to look like a completely different image.
EmbAu: A Novel Technique to Embed Audio Data Using Shuffled Frog Leaping Algorithm
In our currently proposed image steganographic technique, we used the Shuffled Frog Leaping Algorithm (SFLA) to determine the order of pixels by which sensitive information can be placed in the cover image.
EditGuard: Versatile Image Watermarking for Tamper Localization and Copyright Protection
In the era where AI-generated content (AIGC) models can produce stunning and lifelike images, the lingering shadow of unauthorized reproductions and malicious tampering poses imminent threats to copyright integrity and information security.
GhostEncoder: Stealthy Backdoor Attacks with Dynamic Triggers to Pre-trained Encoders in Self-supervised Learning
Pre-trained image encoders can serve as feature extractors, facilitating the construction of downstream classifiers for various tasks.
Securing Fixed Neural Network Steganography
To deal with this issue, we propose a key-based FNNS scheme to improve the security of the FNNS, where we generate key-controlled perturbations from the FNN for data embedding.
Open Image Content Disarm And Reconstruction
In addition, some malware use steganography to hide malicious scripts or sensitive data in images.
StyleStegan: Leak-free Style Transfer Based on Feature Steganography
Our method consists of two main components: a style transfer method that accomplishes artistic stylization on the original image and an image steganography method that embeds content feature secrets on the stylized image.
