The process of information hiding is modeled in the context of additive noise. Under an independence assumption, the histogram of the stegomessage is a convolution of the noise probability mass function (PMF) and the original histogram. In the frequency domain this convolution is viewed as a multiplication of the histogram characteristic function (HCF) and the noise characteristic function. Least significant bit, spread spectrum, and DCT hiding methods for images are analyzed in this framework. It is shown that these embedding methods are equivalent to a lowpass filtering of histograms that is quantified by a decrease in the HCF center of mass (COM). These decreases are exploited in a known scheme detection to classify unaltered and spread spectrum images using a bivariate classifier. Finally, a blind detection scheme is built that uses only statistics from unaltered images. By calculating the Mahalanobis distance from a test COM to the training distribution, a threshold is used to identify steganographic images. At an embedding rate of 1 b.p.p. greater than 95% of the stegoimages are detected with false alarm rate of 5%.

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Steganalysis of additive-noise modelable information hiding

Steganography is the art of hiding the very presence of communication by embedding secret messages into innocuous looking cover documents, such as digital images. Detection of steganography, estimation of message length, and its extraction belong to the field of steganalysis. Steganalysis has recently received a great deal of attention both from law enforcement and the media. In our paper, we classify and review current stego-detection algorithms that can be used to trace popular steganographic products. We recognize several qualitatively different approaches to practical steganalysis - visual detection, detection based on first order statistics (histogram analysis), dual statistics methods that use spatial correlations in images and higher-order statistics (RS steganalysis), universal blind detection schemes, and special cases, such as JPEG compatibility steganalysis. We also present some new results regarding our previously proposed detection of LSB embedding using sensitive dual statistics. The recent steganalytic methods indicate that the most common paradigm in image steganography - the bit-replacement or bit substitution - is inherently insecure with safe capacities far smaller than previously thought.

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Practical steganalysis of digital images: state of the art

Review: A review on blind detection for image steganography

In this paper, we present a new steganographic paradigm for digital images in raster formats. Message bits are embedded in the cover image by adding a weak noise signal with a specified but arbitrary probabilistic distribution. This embedding mechanism provides the user with the flexibility to mask the embedding distortion as noise generated by a particular image acquisition device. This type of embedding will lead to more secure schemes because now the attacker must distinguish statistical anomalies that might be created by the embedding process from those introduced during the image acquisition itself. Unlike previously proposed schemes, this new approach, that we call stochastic modulation, achieves oblivious data transfer without using noise extraction algorithms or error correction. This leads to higher capacity (up to 0.8 bits per pixel) and a convenient and simple implementation with low embedding and extraction complexity. But most importantly, because the embedding noise can have arbitrary properties that approximate a given device noise, the new method offers better security than existing methods. At the end of this paper, we extend stochastic modulation to a content-dependent device noise and we also discuss possible attacks on this scheme based on the most recent advances in steganalysis.

/pdf/digital-image-steganography-using-stochastic-modulation-3p1f04lkzi.pdf

Digital image steganography using stochastic modulation

Steganalysis in the wide sense consists of first identifying suspicious objects and then further analysis during which
we try to identify the steganographic scheme used for embedding, recover the stego key, and finally extract the
hidden message. In this paper, we present a methodology for identifying the stego key in key-dependent
steganographic schemes. Previous approaches for stego key search were exhaustive searches looking for some
recognizable structure (e.g., header) in the extracted bit-stream. However, if the message is encrypted, the search
will become much more expensive because for each stego key, all possible encryption keys would have to be tested.
In this paper, we show that for a very wide range of steganographic schemes, the complexity of the stego key search
is determined only by the size of the stego key space and is independent of the encryption algorithm. The correct
stego key can be determined through an exhaustive stego key search by quantifying statistical properties of samples
along portions of the embedding path. The correct stego key is then identified by an outlier sample distribution.
Although the search methodology is applicable to virtually all steganographic schemes, in this paper we focus on
JPEG steganography. Search techniques for spatial steganographic techniques are treated in our upcoming paper.

Searching for the stego-key

Data hiding in multimedia is the process of secretly embedding information into data sources such as image, video, or audio signals without changing the perceptual quality of the data source. We present a novel blind data hiding technique for hiding information in still images. This technique is based on embedding the information in the transform domain, after decorrelating the samples in the spatial domain. This results in a significant increase in the number of transform coefficients that can be used to transmit the hidden information. The technique is suitable for a variety of data hiding applications such as steganography, data authentication and captioning. The technique achieves a higher and more secure data embedding rate than existing data embedding transform domain techniques developed for these particular applications.

F. Alturki

Papers

A novel approach for increasing security and data embedding capacity in images for data hiding applications