1f:T495,Blind steganalysis based on classifying feature vectors derived from images is becoming increasingly more powerful. For steganalysis of JPEG images, features derived directly in the embedding domain from DCT coefficients appear to achieve the best performance (e.g., the DCT features¹⁰ and Markov features²¹). The goal of this paper is to construct a new multi-class JPEG steganalyzer with markedly improved performance. We do so first by extending the 23 DCT feature set,¹⁰ then applying calibration to the Markov features described in²¹ and reducing their dimension. The resulting feature sets are merged, producing a 274-dimensional feature vector. The new feature set is then used to construct a Support Vector Machine multi-classifier capable of assigning stego images to six popular steganographic algorithms-F5,²² OutGuess,¹⁸ Model Based Steganography without ,¹⁹ and with²⁰ deblocking, JP Hide&Seek,¹ and Steghide.¹⁴ Comparing to our previous work on multi-classification,^{11, 12} the new feature set provides significantly more reliable results.20:T485,This paper proposes a new adaptive least-significant- bit (LSB) steganographic method using pixel-value differencing (PVD) that provides a larger embedding capacity and imperceptible stegoimages. The method exploits the difference value of two consecutive pixels to estimate how many secret bits will be embedded into the two pixels. Pixels located in the edge areas are embedded by a k-bit LSB substitution method with a larger value of k than that of the pixels located in smooth areas. The range of difference values is adaptively divided into lower level, middle level, and higher level. For any pair of consecutive pixels, both pixels are embedded by the k-bit LSB substitution method. However, the value k is adaptive and is decided by the level which the difference value belongs to. In order to remain at the same level where the difference value of two consecutive pixels belongs, before and after embedding, a delicate readjusting phase is used. When compared to the past study of Wu et al.'s PVD and LSB replacement method, our experimental results show that our proposed approach provides both larger embedding capacity and higher image quality.21:T51d,Electronic watermarking can be traced back as far as 1954. The last 10 years has seen considerable interest in digital watermarking, due, in large part, to concerns about illegal piracy of copyrighted content. In this paper, we consider the following questions: is the interest warranted? What are the commercial applications of the technology? What scientific progress has been made in the last 10 years? What are the most exciting areas for research? And where might the next 10 years take us? In our opinion, the interest in watermarking is appropriate. However, we expect that copyright applications will be overshadowed by applications such as broadcast monitoring, authentication, and tracking content distributed within corporations. We further see a variety of applications emerging that add value to media, such as annotation and linking content to the Web. These latter applications may turn out to be the most compelling. Considerable progress has been made toward enabling these applications—perceptual modelling, security threats and countermeasures, and the development of a bag of tricks for efficient implementations. Further progress is needed in methods for handling geometric and temporal distortions. We expect other exciting developments to arise from research in informed watermarking.

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