Digital Image Steganalysis: Current Methodologies and Future Challenges

doi:10.1109/ACCESS.2022.3202905

Digital Image Steganalysis: Current Methodologies and Future Challenges

2022-01-01

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DOI：10.1109/ACCESS.2022.3202905

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With the growing use of the internet and social media, data security has become a major issue. Thus, researchers are focusing on data security techniques such as steganography and steganalysis. Steganography is the approach of concealing the existence of secret messages in digital media for secure transmission. Steganalysis techniques aim to detect the existence of concealed messages and extract them. Digital image steganography and steganalysis techniques are classified into the spatial and transform domains. In this paper, we provide a detailed survey of the state-of-the-art works that have been performed in two-dimensional and three-dimensional image steganalysis. We present the most popular datasets and explain some steganographic methods for embedding hidden data. Steganalysis is a very difficult task due to the lack of information about the characteristics of the cover media that can be exploited to detect hidden messages. Therefore, we review studies performed on image steganalysis in the spatial and transform domains using classical machine learning and deep learning approaches. Additionally, we present open challenges and discuss some directions for future research.

Keywords:

Steganography

Transforms

Three-dimensional displays

Feature extraction

Media

Deep learning

Discrete cosine transforms

Machine learning

Steganography

steganalysis

deep learning

machine learning

Digital Image Steganalysis: Current Methodologies and Future Challenges

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