1,568 research outputs found
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. This leads to small payload capacity
and image distortion. Here we propose a convolutional neural network based
encoder-decoder architecture for embedding of images as payload. To this end,
we make following three major contributions: (i) we propose a deep learning
based generic encoder-decoder architecture for image steganography; (ii) we
introduce a new loss function that ensures joint end-to-end training of
encoder-decoder networks; (iii) we perform extensive empirical evaluation of
proposed architecture on a range of challenging publicly available datasets
(MNIST, CIFAR10, PASCAL-VOC12, ImageNet, LFW) and report state-of-the-art
payload capacity at high PSNR and SSIM values
New Classification Methods for Hiding Information into Two Parts: Multimedia Files and Non Multimedia Files
With the rapid development of various multimedia technologies, more and more
multimedia data are generated and transmitted in the medical, commercial, and
military fields, which may include some sensitive information which should not
be accessed by or can only be partially exposed to the general users.
Therefore, security and privacy has become an important, Another problem with
digital document and video is that undetectable modifications can be made with
very simple and widely available equipment, which put the digital material for
evidential purposes under question .With the large flood of information and the
development of the digital format Information hiding considers one of the
techniques which used to protect the important information. The main goals for
this paper, provides a general overview of the New Classification Methods for
Hiding Information into Two Parts: Multimedia Files and Non Multimedia Files
Invisible Steganography via Generative Adversarial Networks
Nowadays, there are plenty of works introducing convolutional neural networks
(CNNs) to the steganalysis and exceeding conventional steganalysis algorithms.
These works have shown the improving potential of deep learning in information
hiding domain. There are also several works based on deep learning to do image
steganography, but these works still have problems in capacity, invisibility
and security. In this paper, we propose a novel CNN architecture named as
\isgan to conceal a secret gray image into a color cover image on the sender
side and exactly extract the secret image out on the receiver side. There are
three contributions in our work: (i) we improve the invisibility by hiding the
secret image only in the Y channel of the cover image; (ii) We introduce the
generative adversarial networks to strengthen the security by minimizing the
divergence between the empirical probability distributions of stego images and
natural images. (iii) In order to associate with the human visual system
better, we construct a mixed loss function which is more appropriate for
steganography to generate more realistic stego images and reveal out more
better secret images. Experiment results show that ISGAN can achieve
start-of-art performances on LFW, Pascal VOC2012 and ImageNet datasets.Comment: 13 pages, 7 figure
Deep Learning in steganography and steganalysis from 2015 to 2018
For almost 10 years, the detection of a hidden message in an image has been
mainly carried out by the computation of Rich Models (RM), followed by
classification using an Ensemble Classifier (EC). In 2015, the first study
using a convolutional neural network (CNN) obtained the first results of
steganalysis by Deep Learning approaching the performances of the two-step
approach (EC + RM). Between 2015-2018, numerous publications have shown that it
is possible to obtain improved performances, notably in spatial steganalysis,
JPEG steganalysis, Selection-Channel-Aware steganalysis, and in quantitative
steganalysis. This chapter deals with deep learning in steganalysis from the
point of view of current methods, by presenting different neural networks from
the period 2015-2018, that have been evaluated with a methodology specific to
the discipline of steganalysis. The chapter is not intended to repeat the basic
concepts of machine learning or deep learning. So, we will present the
structure of a deep neural network, in a generic way and present the networks
proposed in existing literature for the different scenarios of steganalysis,
and finally, we will discuss steganography by deep learning.Comment: Book chapter, final version (October 2019). This chapter will appear
in 2020 in the book titled: "Digital Media Steganography: Principles,
Algorithms, Advances", Book Editor: M. Hassaballah. 46 page
HiDDeN: Hiding Data With Deep Networks
Recent work has shown that deep neural networks are highly sensitive to tiny
perturbations of input images, giving rise to adversarial examples. Though this
property is usually considered a weakness of learned models, we explore whether
it can be beneficial. We find that neural networks can learn to use invisible
perturbations to encode a rich amount of useful information. In fact, one can
exploit this capability for the task of data hiding. We jointly train encoder
and decoder networks, where given an input message and cover image, the encoder
produces a visually indistinguishable encoded image, from which the decoder can
recover the original message. We show that these encodings are competitive with
existing data hiding algorithms, and further that they can be made robust to
noise: our models learn to reconstruct hidden information in an encoded image
despite the presence of Gaussian blurring, pixel-wise dropout, cropping, and
JPEG compression. Even though JPEG is non-differentiable, we show that a robust
model can be trained using differentiable approximations. Finally, we
demonstrate that adversarial training improves the visual quality of encoded
images
EncryptGAN: Image Steganography with Domain Transform
We propose an image steganographic algorithm called EncryptGAN, which
disguises private image communication in an open communication channel. The
insight is that content transform between two very different domains (e.g.,
face to flower) allows one to hide image messages in one domain (face) and
communicate using its counterpart in another domain (flower). The key
ingredient in our method, unlike related approaches, is a specially trained
network to extract transformed images from both domains and use them as the
public and private keys. We ensure the image communication remain secret except
for the intended recipient even when the content transformation networks are
exposed.
To communicate, one directly pastes the `message' image onto a larger public
key image (face). Depending on the location and content of the message image,
the `disguise' image (flower) alters its appearance and shape while maintaining
its overall objectiveness (flower). The recipient decodes the alternated image
to uncover the original image message using its message image key. We implement
the entire procedure as a constrained Cycle-GAN, where the public and the
private key generating network is used as an additional constraint to the cycle
consistency. Comprehensive experimental results show our EncryptGAN outperforms
the state-of-arts in terms of both encryption and security measures.Comment: 11pages, 6 figure
Real-Time Steganalysis for Stream Media Based on Multi-channel Convolutional Sliding Windows
Previous VoIP steganalysis methods face great challenges in detecting speech
signals at low embedding rates, and they are also generally difficult to
perform real-time detection, making them hard to truly maintain cyberspace
security. To solve these two challenges, in this paper, combined with the
sliding window detection algorithm and Convolution Neural Network we propose a
real-time VoIP steganalysis method which based on multi-channel convolution
sliding windows. In order to analyze the correlations between frames and
different neighborhood frames in a VoIP signal, we define multi channel sliding
detection windows. Within each sliding window, we design two feature extraction
channels which contain multiple convolution layers with multiple convolution
kernels each layer to extract correlation features of the input signal. Then
based on these extracted features, we use a forward fully connected network for
feature fusion. Finally, by analyzing the statistical distribution of these
features, the discriminator will determine whether the input speech signal
contains covert information or not.We designed several experiments to test the
proposed model's detection ability under various conditions, including
different embedding rates, different speech length, etc. Experimental results
showed that the proposed model outperforms all the previous methods, especially
in the case of low embedding rate, which showed state-of-the-art performance.
In addition, we also tested the detection efficiency of the proposed model, and
the results showed that it can achieve almost real-time detection of VoIP
speech signals.Comment: 13 pages, summit to ieee transactions on information forensics and
security (tifs
Digital Cardan Grille: A Modern Approach for Information Hiding
In this paper, a new framework for construction of Cardan grille for
information hiding is proposed. Based on the semantic image inpainting
technique, the stego image are driven by secret messages directly. A mask
called Digital Cardan Grille (DCG) for determining the hidden location is
introduced to hide the message. The message is written to the corrupted region
that needs to be filled in the corrupted image in advance. Then the corrupted
image with secret message is feeded into a Generative Adversarial Network (GAN)
for semantic completion. The adversarial game not only reconstruct the
corrupted image , but also generate a stego image which contains the logic
rationality of image content. The experimental results verify the feasibility
of the proposed method
Steganalysis via a Convolutional Neural Network using Large Convolution Filters for Embedding Process with Same Stego Key
For the past few years, in the race between image steganography and
steganalysis, deep learning has emerged as a very promising alternative to
steganalyzer approaches based on rich image models combined with ensemble
classifiers. A key knowledge of image steganalyzer, which combines relevant
image features and innovative classification procedures, can be deduced by a
deep learning approach called Convolutional Neural Networks (CNN). These kind
of deep learning networks is so well-suited for classification tasks based on
the detection of variations in 2D shapes that it is the state-of-the-art in
many image recognition problems. In this article, we design a CNN-based
steganalyzer for images obtained by applying steganography with a unique
embedding key. This one is quite different from the previous study of {\em Qian
et al.} and its successor, namely {\em Pibre et al.} The proposed architecture
embeds less convolutions, with much larger filters in the final convolutional
layer, and is more general: it is able to deal with larger images and lower
payloads. For the "same embedding key" scenario, our proposal outperforms all
other steganalyzers, in particular the existing CNN-based ones, and defeats
many state-of-the-art image steganography schemes
Offline Arabic Handwriting Recognition Using Artificial Neural Network
The ambition of a character recognition system is to transform a text
document typed on paper into a digital format that can be manipulated by word
processor software Unlike other languages, Arabic has unique features, while
other language doesn't have, from this language these are seven or eight
language such as ordo, jewie and Persian writing, Arabic has twenty eight
letters, each of which can be linked in three different ways or separated
depending on the case. The difficulty of the Arabic handwriting recognition is
that, the accuracy of the character recognition which affects on the accuracy
of the word recognition, in additional there is also two or three from for each
character, the suggested solution by using artificial neural network can solve
the problem and overcome the difficulty of Arabic handwriting recognition.Comment: Submitted to Journal of Computer Science and Engineering, see
http://sites.google.com/site/jcseuk/volume-1-issue-1-may-201
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