Abstract
This work tackles a recent challenge in digital image processing: how to identify the steganographic images from a steganographer, who is unknown among multiple innocent actors. The method does not need a large number of samples to train classification model, and thus it is significantly different from the traditional steganalysis. The proposed scheme consists of textural features and clustering ensembles. Local ternary patterns (LTP) are employed to design low-dimensional textural features which are considered to be more sensitive to steganographic changes in texture regions of image. Furthermore, we use the extracted low-dimensional textural features to train a number of hierarchical clustering results, which are integrated as an ensemble based on the majority voting strategy. Finally, the ensemble is used to make optimal decision for suspected image. Extensive experiments show that the proposed scheme is effective and efficient and outperforms the state-of-the-art steganalysis methods with an average gain from 4 % to 6 % .
Highlights
Steganalysis, as a countermeasure for steganography, aims to detect the presence of hidden data in a digital media, such as digital images, video or audio files
We term this new problem as multi-source stego detection problem, which poses a special challenge to digital image processing due to the above reasons. We tackle this problem with (1) low-dimensional textural features and (2) clustering ensemble applied to multi-source steganographic targets. In the former, we develop the local ternary patterns to design high-dimensional textural feature set, whose dimension is subsequently reduced to an appropriate level according to feature correlation
We addressed the multi-source stego detection problem and proposed a new steganographic image identification scheme with unsupervised learning approach, which is significantly different from steganographer detection and traditional stego detection
Summary
Steganalysis, as a countermeasure for steganography, aims to detect the presence of hidden data in a digital media, such as digital images, video or audio files. In most steganalytic techniques [1,2,3,4], one can extract sensitive feature sets (low-dimensional [5,6] or high-dimensional [7,8,9,10,11]) from large datasets of digital images, which include original and steganographic images. The problem of detecting hidden data is usually restricted in scenarios where only a single actor (or equivalently a user) is considered, i.e., to detect whether or not objects from the same user are cover or stego. We call this problem as the stego detection problem. This new problem is termed as the steganographer detection problem and has been recently studied in [14,15,16,17]
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