2 research outputs found
Recent Advances of Image Steganography with Generative Adversarial Networks
In the past few years, the Generative Adversarial Network (GAN) which
proposed in 2014 has achieved great success. GAN has achieved many research
results in the field of computer vision and natural language processing. Image
steganography is dedicated to hiding secret messages in digital images, and has
achieved the purpose of covert communication. Recently, research on image
steganography has demonstrated great potential for using GAN and neural
networks. In this paper we review different strategies for steganography such
as cover modification, cover selection and cover synthesis by GANs, and discuss
the characteristics of these methods as well as evaluation metrics and provide
some possible future research directions in image steganography.Comment: 39 pages, 26 figure
Robust Invisible Hyperlinks in Physical Photographs Based on 3D Rendering Attacks
In the era of multimedia and Internet, people are eager to obtain information
from offline to online. Quick Response (QR) codes and digital watermarks help
us access information quickly. However, QR codes look ugly and invisible
watermarks can be easily broken in physical photographs. Therefore, this paper
proposes a novel method to embed hyperlinks into natural images, making the
hyperlinks invisible for human eyes but detectable for mobile devices. Our
method is an end-to-end neural network with an encoder to hide information and
a decoder to recover information. From original images to physical photographs,
camera imaging process will introduce a series of distortion such as noise,
blur, and light. To train a robust decoder against the physical distortion from
the real world, a distortion network based on 3D rendering is inserted between
the encoder and the decoder to simulate the camera imaging process. Besides, in
order to maintain the visual attraction of the image with hyperlinks, we
propose a loss function based on just noticeable difference (JND) to supervise
the training of encoder. Experimental results show that our approach
outperforms the previous method in both simulated and real situations