Abstract. In this paper, we present a steganalytic method that can reliably detect messages (and estimate their size) hidden in JPEG images using the steganographic algorithm F5. The key element of the method is estimation of the cover-image histogram from the stego-image. This is done by decompressing the stego-image, cropping it by four pixels in both directions to remove the quantization in the frequency domain, and recompressing it using the same quality factor as the stego-image. The number of relative changes introduced by F5 is determined using the least square fit by comparing the estimated histograms of selected DCT coefficients with those of the stegoimage. Experimental results indicate that relative modifications as small as 10% of the usable DCT coefficients can be reliably detected. The method is tested on a diverse set of test images that include both raw and processed images in the JPEG and BMP formats. Overview of Steganography and SteganalysisSteganography is the art of invisible communication. Its purpose is to hide the very presence of communication by embedding messages into innocuous-looking cover objects. In today's digital world, invisible ink and paper have been replaced by much more versatile and practical covers for hiding messages -digital documents, images, video, and audio files. As long as an electronic document contains perceptually irrelevant or redundant information, it can be used as a "cover" for hiding secret messages. In this paper, we deal solely with covers that are digital images stored in the JPEG format.Each steganographic communication system consists of an embedding algorithm and an extraction algorithm. To accommodate a secret message, the original image, also called the cover-image, is slightly modified by the embedding algorithm. As a result, the stego-image is obtained.Steganalysis is the art of discovering hidden data in cover objects. As in cryptanalysis, we assume that the steganographic method is publicly known with the exception of a secret key. The method is secure if the stego-images do not contain any
The objective of steganalysis is to detect messages hidden in cover objects, such as digital images. In practice, the steganalyst is frequently interested in more than whether or not a secret message is present. The ultimate goal is to extract and decipher the secret message. However, in the absence of the knowledge of the stego technique and the stego and cipher keys, this task may be extremely time consuming or completely infeasible. Therefore, any additional information, such as the message length or its approximate placement in image features, could prove very valuable to the analyst. In this paper, we present general principles for developing steganalytic methods that can accurately estimate the number of changes to the cover image imposed during embedding. Using those principles, we show how to estimate the secret message length for the most common embedding archetypes, including the F5 and OutGuess algorithms for JPEG, EzStego algorithm with random straddling for palette images, and the classical LSB embedding with random straddling for uncompressed image formats. The paper concludes with an outline of ideas for future research such as estimating the steganographic capacity of embedding algorithms. Basics of steganography and steganalysisThe purpose of steganography is to communicate information in a stealth manner so that anyone who inspects the messages being exchanged cannot collect enough evidence that the messages hide additional secret data. As opposed to cryptography that makes the communication unintelligible to those who do not know the proper cipher keys, steganography makes the communication inconspicuous or invisible. An old example of steganography is writing messages between the lines of an ordinary letter using invisible ink. Another simple technique is to use tiny markers (pinholes) to mark letters in a text. A very well-written historical perspective on steganography can be found in [20]. In today's digital world, invisible ink and paper have been replaced by much more versatile and practical covers for hiding messages -digital documents, images, video, and audio files. All electronic documents containing perceptually irrelevant or redundant information provide a good environment for steganographic communication. The object that holds the secret information is called the cover object. After a secret message has been hidden in the cover, the cover object becomes the stego object.
In this paper, we present general methodology for developing attacks on steganographic systems for the JPEG image format. The detection first starts by decompressing the JPEG stego image, geometrically distorting it (e.g., by cropping), and recompressing. Because the geometrical distortion breaks the quantized structure of DCT coefficients during recompression, the distorted/recompressed image will have many macroscopic statistics approximately equal to those of the cover image. We choose such macroscopic statistic S that also predictably changes with the embedded message length. By doing so, we estimate the unknown message length by comparing the values of S for the stego image and the cropped/recompressed stego image. The details of this detection methodology are explained on the F5 algorithm and OutGuess. The accuracy of the message length estimate is demonstrated on test images for both algorithms. Finally, we identify two limitations of the proposed approach and show how they can be overcome to obtain accurate detection in every case. The paper is closed with outlining a condition that must be satisfied by all secure high-capacity steganographic algorithms for JPEGs.Keywords: Steganalysis, steganography, attacks, JPEG STEGANOGRAPHY FOR JPEG IMAGESThe JPEG format is currently the most common format for storing image data. It is also supported by virtually all software applications that allow viewing and working with digital images. Recently, several steganographic techniques for data hiding in JPEGs have been developed: J-Steg . In all programs, message bits are embedded by manipulating the quantized DCT coefficients. J-Steg and OutGuess embed message bits into the LSBs of quantized DCT coefficients, while F5 always decrements the values in order to modify the LSBs. J-Steg with sequential message embedding is detectable using the chi-square attack 15 . J-Steg with random straddling as well as JP Hide&Seek are detectable using the generalized chi-square attack 11,13 . The chi-square attacks are not effective for F5 (because F5 does not flip LSBs) and for OutGuess (OutGuess preserves first-order statistics). The universal blind detector pioneered by Farid 6 seems to be able to detect most steganographic methods after appropriate training on a database of stego and cover images. However, at the time of writing this paper, the blind detector does not naturally allow accurate estimation of the length of the embedded messages and it is not clear how its performance scales to more diverse databases. Also, this detector cannot detect messages embedded using F5 in grayscale images. The authors of this paper also believe that detection methods targeted to a specific steganographic technique will necessarily give better accuracy and detection reliability that blind approaches. Another important advantage of the approach proposed in this paper compared to previously introduced detection schemes is that one can obtain an accurate estimate for the length of the embedded secret message.In this paper, we will positi...
Comdb2 is a distributed database system designed for geographical replication and high availability. In contrast with the latest trends in this field, Comdb2 o↵ers full transactional support, a standard relational model, and the expressivity of SQL. Moreover, the system allows for rich stored procedures using a dialect of Lua. Comdb2 implements a serializable system in which reads from any node always return current values. Comdb2 provides transparent High Availability through built-in service discovery and sophisticated retry logic embedded in the standard API. In addition to the relational data model, Comdb2 implements queues for publisher-to-subscriber message delivery. Queues can be combined with table triggers for timeconsistent log distribution, providing functionality commonly needed in modern OLTP. In this paper we give an overview of our last twelve years of work. We focus on the design choices that have made Comdb2 the primary database solution within our company, Bloomberg LP (BLP).
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