Image adaptive steganalysis system and method based on saliency detection

Abstract

The invention belongs to the technical field of image processing, and particularly discloses an image adaptive steganalysis system based on saliency detection, which comprises a saliency detection module, a region screening module and a discriminator module. The invention also discloses an analysis method, which specifically comprises the following steps of: firstly, inputting an image detected tobe wrong by the discriminator module into the saliency detection module to form a saliency map; extracting a saliency map meeting requirements by the region screening module, and performing image fusion on the saliency map and a corresponding original image to form a saliency fusion map; and finally, replacing the saliency map which does not meet the requirements with the original image, combining the original image and the saliency fusion map to form an updated data set, and inputting the updated data set into the discriminator module for training to enable the discriminator to perform targeted feature learning on an area with relatively high overlap ratio with the steganography area. According to the method, the saliency detection technology is utilized to guide the steganalysis model to pay more attention to the characteristics of the image steganography area, so that the training effect and the detection accuracy of the model are improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an image adaptive steganalysis system and method based on saliency detection. Background technique [0002] Image steganography is a covert communication technology that embeds secret messages in image carrier files for transmission. Unlike traditional encrypted communication methods that make it difficult for third parties to decipher, image steganography conceals the communication behavior itself, making it difficult for third parties to decipher. It detects the embedding of secret messages, so it is highly deceptive. Image steganalysis is specially used to detect messages embedded using image steganography, and image steganography is a pair of mutual games and promotes each other during the game Developed technology. The difficulty of image steganalysis lies in the fact that the steganographic operation embeds very little information in the image, and the slight differe...

AI Technical Summary

Problems solved by technology

The most commonly used adaptive steganography algorithms are HUGO, WOW, S-UNIWARD, UED, and J-UNIWARD. The modification probability of elements located in complex texture areas in the image is greater than that of elements located in smooth areas. The reason is that the steganographic algorithm is in these statistical Disturbances caused by regular and complex areas are more difficult to detect

[0004] However, most of the existing image steganalysis research work is to improve the network structure to improve the detection performance of steganalysis based on the network model, but in the use of image adaptive steganalysis When steganography is used for steganography, the secret message is not embedded in all areas of the image, and the image contains rich information dimensions, so the information contained in the image is not completely beneficial to the training. This situation makes the model unnecessary during training. The redundant interference, resulting in lower detection accuracy

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