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Traditional encrypted communication technologies have been easily detected and have struggled to meet the needs of secure communication. Steganography, capable of hiding information by modifying the carrier, has been utilized to realize covert communication. However, the potential for steganography to be employed in illegal acts has directed increasing attention towards steganalysis for the detection of steganography, thereby bestowing great research significance upon it. Deep learning has yielded numerous research achievements in fields such as computer vision, pattern recognition, and natural language processing, introducing new opportunities and challenges to steganalysis. These advancements have propelled the generation of new ideas and methods in steganalysis. Currently, color images constitute the mainstream carrier in the process of internet transmission. Nevertheless, existing steganalysis features for color images primarily rely on manual design and often treat the color image as three independent grayscale images, without fully considering the internal relationships between the three color channels, thus necessitating an improvement in detection capabilities for encrypted images. The application of deep learning in the field of color image steganalysis remains in its preliminary stage. The concepts, classifications, and research significance of steganography and steganalysis were introduced, along with an outline of their current research status. Several key techniques for the steganalysis of color images were introduced, compared, summarized, and their development trends were analyzed. |
