403 research outputs found
On the Gold Standard for Security of Universal Steganography
While symmetric-key steganography is quite well understood both in the
information-theoretic and in the computational setting, many fundamental
questions about its public-key counterpart resist persistent attempts to solve
them. The computational model for public-key steganography was proposed by von
Ahn and Hopper in EUROCRYPT 2004. At TCC 2005, Backes and Cachin gave the first
universal public-key stegosystem - i.e. one that works on all channels -
achieving security against replayable chosen-covertext attacks (SS-RCCA) and
asked whether security against non-replayable chosen-covertext attacks (SS-CCA)
is achievable. Later, Hopper (ICALP 2005) provided such a stegosystem for every
efficiently sampleable channel, but did not achieve universality. He posed the
question whether universality and SS-CCA-security can be achieved
simultaneously. No progress on this question has been achieved since more than
a decade. In our work we solve Hopper's problem in a somehow complete manner:
As our main positive result we design an SS-CCA-secure stegosystem that works
for every memoryless channel. On the other hand, we prove that this result is
the best possible in the context of universal steganography. We provide a
family of 0-memoryless channels - where the already sent documents have only
marginal influence on the current distribution - and prove that no
SS-CCA-secure steganography for this family exists in the standard
non-look-ahead model.Comment: EUROCRYPT 2018, llncs styl
Suitability of lacunarity measure for blind steganalysis
Blind steganalysis performance is influenced by several factors including the features used for classification. This paper investigates the suitability of using lacunarity measure as a potential feature vectorfor blind steganalysis. Differential Box Counting (DBC) based lacunarity measure has been employed using the traditional sequential grid (SG) and a new radial strip (RS) approach. The performance of the multi-class SVM based classifier was unfortunately not what was expected. However, the findings show that both the SG and RS lacunarity produce enough discriminating features that warrant further research
American standard code for information interchange mapping technique for text hiding in the RGB and gray images
One of the significant techniques for hiding important information (such as text, image, and audio) is steganography. Steganography is used to keep this information as secret as possible, especially the sensitive ones after the massive expansion of data transmission through the Internet inside a conventional, non-secret, file, or message. This paper uses the American standard code for information interchange (ASCII) mapping technique (AMT) to hide the data in the color and grey image by converting it in a binary form, also convert the three levels of the red, green, and blue (RGB) image and grey image in the binary form, and then hide the data through hiding every two bits of the text in the two bits of one of the levels from the RGB image and grey image that means the text will be distributed throughout the images and allows hiding large amounts of data. That will send the information in a good securing way
Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography
Data hiding is the process of embedding information into a noise-tolerant
signal such as a piece of audio, video, or image. Digital watermarking is a
form of data hiding where identifying data is robustly embedded so that it can
resist tampering and be used to identify the original owners of the media.
Steganography, another form of data hiding, embeds data for the purpose of
secure and secret communication. This survey summarises recent developments in
deep learning techniques for data hiding for the purposes of watermarking and
steganography, categorising them based on model architectures and noise
injection methods. The objective functions, evaluation metrics, and datasets
used for training these data hiding models are comprehensively summarised.
Finally, we propose and discuss possible future directions for research into
deep data hiding techniques
Image steganography using least significant bit and secret map techniques
In steganography, secret data are invisible in cover media, such as text, audio, video and image. Hence, attackers have no knowledge of the original message contained in the media or which algorithm is used to embed or extract such message. Image steganography is a branch of steganography in which secret data are hidden in host images. In this study, image steganography using least significant bit and secret map techniques is performed by applying 3D chaotic maps, namely, 3D Chebyshev and 3D logistic maps, to obtain high security. This technique is based on the concept of performing random insertion and selecting a pixel from a host image. The proposed algorithm is comprehensively evaluated on the basis of different criteria, such as correlation coefficient, information entropy, homogeneity, contrast, image, histogram, key sensitivity, hiding capacity, quality index, mean square error (MSE), peak signal-to-noise ratio (PSNR) and image fidelity. Results show that the proposed algorithm satisfies all the aforementioned criteria and is superior to other previous methods. Hence, it is efficient in hiding secret data and preserving the good visual quality of stego images. The proposed algorithm is resistant to different attacks, such as differential and statistical attacks, and yields good results in terms of key sensitivity, hiding capacity, quality index, MSE, PSNR and image fidelity
- …