Digital Image Steganalysis: Current Methodologies and Future Challenges

• Published in: IEEE access Vol. 10; pp. 92321 - 92336
• Main Authors: Eid, Wafa M., Alotaibi, Sarah S., Alqahtani, Hasna M., Saleh, Sahar Q.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Deep learning; Digital imaging; Digital media; Discrete cosine transforms; Domains; Feature extraction; Machine learning; Media; Messages; Security; steganalysis; Steganography; Three-dimensional displays; Transforms
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3202905

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.