Objective

2

Image steganalysis is the opposite technology of steganography; it aims to detect

extract

restore

and destroy secret messages embedded in cover images.As an important technical tool for image information security

image steganalysis has become popular in multimedia information security to researchers all over the world.The basic concept of the current image steganalysis is to analyze the embedding mechanism and the statistical changes in image data caused by embedding secret messages.Images steganalysis overcomes the binary classification problem by using the cover and stego images of two image categories.The performance of steganalysis methods depends on feature extraction

and steganalysis features are expected to have small within-class scatter distances and big between-class scatter distances.However

embedded changes are not only correlated with steganography methods but also with image content and local statistical characteristics.The changes in steganalysis features caused by secret embedding are subtle

especially when the embedding ratio is low.The contents and statistical characteristics of images have a stronger impact on the distribution of steganalysis features than the embedding process.Thus

the steganalysis features of cover and stego images are inseparable

a scenario that can be attributed to the differences in image statistical characteristics.Consequently

image steganalysis becomes a classification problem with large within-class and small between-class scatter distances.To solve this problem

a new steganalysis framework for JPEG images

which aims to reduce the within-class scatter distances

is proposed.

Method

2

The secret messages after embedding will have different effects on the characteristics of images with different content complexities

while the steganalysis features of the images with the same content complexity are similar.This study on image steganalysis focuses on reducing the differences of image statistical characteristics caused by various contents and processing methods.The motivation of the new model is introduced by analyzing the Fisher linear discriminant analysis

which is the basis of the ensemble classifier

the most used one in steganalysis applications

and a new steganalysis model of JPEG images based on image classification and segmentation is proposed.We define a content complexity evaluation feature for each image

and the given images are first classified according to the content.Thus

the images classified to the same sub-class will have a closer content complexity.Then

each image is segmented to several sub-images according to the evaluated texture features and the complexity of each sub-block.During segmentation

we first categorize the image blocks according to texture complexity

and then amalgamate the adjacent block categories.After the combined classification and segmentation process

the content texture of the same class of image regions is more similar

and the steganalysis features are more centralized.The steganalysis features are extracted separately from each subset with the same or close texture complexity to build a classifier.When deciding which steganalysis feature set to extract

we mainly consider the performance.In our prior work

we found that when extracting a steganalysis feature set with low dimension

the performance of the method based on classification or segmentation can be obviously improved.However

when extracting high-dimensional steganalysis features

such as JPEG rich model (JRM)

the performance is unsatisfactory because the rich model is based on the residual of the given image

and it can eliminate the effect of image content.The JRM feature set is sensitive to subtle image details

and the steganalysis result is good.However

we still extract the JRM feature set

which is the most representative high-dimensional feature set in the JPEG domain

to prove the validity of the proposed model.In the testing phase

the steganalysis features of each segmented sub-image in each sub-class are sent to the corresponding classifier.The final steganalysis result is obtained through the weighted fusing process.

Result

2

In the experiment

we compute two kinds of separability criteria of the tested steganalysis feature set

including the separability criterion based on the within-and between-class distances and the Bhattacharyya distances.The Bhattacharyya distance is one of the most used separability criteria on the basis of the probability density of classified samples.Both separability criteria of the proposed method are obviously improved

which means that the proposed classified and segmentation-based steganalysis features can be more easily categorized

thereby verifying the validity of the proposed steganalysis model.We also compare the classification performance of the proposed method and the prior work in various experimental circumstances

including the use of the same and different training and testing image databases.We compute the detection results for the original feature set

the features extracted from the classified image and the segmented image

and the image combined classification and segmentation.Experimental results show that in both circumstances

the combined classification and segmentation process can effectively improve the performance by up to 10%.The improvement considerably higher when the training and testing images have different statistical features

which implies that the proposed method is suitable for practical application on images from the Internet with considerable diversity in sources

processing methods

and contents.

Conclusion

2

In this paper

a new steganalysis model for JPEG images is proposed.The differences in image statistical characteristics caused by various contents and processing methods are reduced by image classification and segmentation.The JRM feature set was extracted.The theoretical analysis of and the experimental results for several diverse image databases and circumstances demonstrate the validity of the framework.When a considerable diversity in image sources and contents exists

such as different training and testing images

the performance improvement of the proposed method is obvious

indicating that the performance of the proposed method does not depending highly on image content.Furthermore

the proposed steganalysis model is suitable for practical application in complex network environments.