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