Abstract
Steganography is the process of hiding data into public digital medium for secret communication. The image in which the secret data is hidden is termed as stego image. The detection of hidden embedded data in the image is the foundation for blind image steganalysis. The appropriate selection of cover file type and composition contribute to the successful embedding. A large number of steganalysis techniques are available for the detection of steganography in the image. The performance of the steganalysis technique depends on the ability to extract the discriminative features for the identification of statistical changes in the image due to the embedded data. The issue encountered in the blind image steganography is the non-availability of knowledge about the applied steganography techniques in the images. This paper surveys various steganalysis methods, different filtering based preprocessing methods, feature extraction methods, and machine learning based classification methods, for the proper identification of steganography in the image.
Highlights
The steganography is a process of making the presence of secret data undetectable in a carrier [1]
These methods are classified into following categories: Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT), Lossless or reversible method, and Embedding methods
The secret information hidden in the image or any other digital media, which is invisible to human, causes the modification of media properties that produces the degradation, unusual characteristics and patterns
Summary
The steganography is a process of making the presence of secret data undetectable in a carrier [1]. The image steganalysis method utilizes the features that are affected by steganography and a machine learning classification. To use this method, the steganalyst must extract the features from a training data set to train the classifier. If different cover source is used, the obtained data set from feature extraction process is different and the classification results are degraded. Transform domain has advantages over spatial domain as the information hidden in the image is will not be affected by compression, cropping, and image processing These methods are classified into following categories: Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT), Lossless or reversible method, and Embedding methods.
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