58 research outputs found
Steganographic Generative Adversarial Networks
Steganography is collection of methods to hide secret information ("payload")
within non-secret information "container"). Its counterpart, Steganalysis, is
the practice of determining if a message contains a hidden payload, and
recovering it if possible. Presence of hidden payloads is typically detected by
a binary classifier. In the present study, we propose a new model for
generating image-like containers based on Deep Convolutional Generative
Adversarial Networks (DCGAN). This approach allows to generate more
setganalysis-secure message embedding using standard steganography algorithms.
Experiment results demonstrate that the new model successfully deceives the
steganography analyzer, and for this reason, can be used in steganographic
applications.Comment: 15 pages, 10 figures, 5 tables, Workshop on Adversarial Training
(NIPS 2016, Barcelona, Spain
Application of Steganography for Anonymity through the Internet
In this paper, a novel steganographic scheme based on chaotic iterations is
proposed. This research work takes place into the information hiding security
framework. The applications for anonymity and privacy through the Internet are
regarded too. To guarantee such an anonymity, it should be possible to set up a
secret communication channel into a web page, being both secure and robust. To
achieve this goal, we propose an information hiding scheme being stego-secure,
which is the highest level of security in a well defined and studied category
of attacks called "watermark-only attack". This category of attacks is the best
context to study steganography-based anonymity through the Internet. The
steganalysis of our steganographic process is also studied in order to show it
security in a real test framework.Comment: 14 page
Text hiding in text using invisible character
Steganography can be defined as the art and science of hiding information in the data that could be read by computer. This science cannot recognize stego-cover and the original one whether by eye or by computer when seeing the statistical samples. This paper presents a new method to hide text in text characters. The systematic method uses the structure of invisible character to hide and extract secret texts. The creation of secret message comprises four main stages such using the letter from the original message, selecting the suitable cover text, dividing the cover text into blocks, hiding the secret text using the invisible character and comparing the cover-text and stego-object. This study uses an invisible character (white space) position of in the cover text that used to hide the the secrete sender masseges. The experiments results show that the suggested method presents highly secret due to use the multi-level of complexity to avoid the attackers
SSGAN: Secure Steganography Based on Generative Adversarial Networks
In this paper, a novel strategy of Secure Steganograpy based on Generative
Adversarial Networks is proposed to generate suitable and secure covers for
steganography. The proposed architecture has one generative network, and two
discriminative networks. The generative network mainly evaluates the visual
quality of the generated images for steganography, and the discriminative
networks are utilized to assess their suitableness for information hiding.
Different from the existing work which adopts Deep Convolutional Generative
Adversarial Networks, we utilize another form of generative adversarial
networks. By using this new form of generative adversarial networks,
significant improvements are made on the convergence speed, the training
stability and the image quality. Furthermore, a sophisticated steganalysis
network is reconstructed for the discriminative network, and the network can
better evaluate the performance of the generated images. Numerous experiments
are conducted on the publicly available datasets to demonstrate the
effectiveness and robustness of the proposed method
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