MI-STEG:A Medical Image Steganalysis Framework Based on Ensemble Deep Learning

Medical image steganography aims to increase data security by concealing patient-personal information as well as diagnostic and therapeutic data in the spatial or frequency domain of radiological images.On the other hand,the discipline of image steganalysis generally provides a classification based on whether an image has hidden data or not.Inspired by previous studies on image steganalysis,this study proposes a deep ensemble learning model for medical image steganalysis to detect malicious hidden data in medical images and develop medical image steganography methods aimed at securing personal information.With this purpose in mind,a dataset containing brain Magnetic Resonance(MR)images of healthy individuals and epileptic patients was built.Spatial Version of the Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Stego(HUGO),and Minimizing the Power of Optimal Detector(MIPOD)techniques used in spatial image steganalysis were adapted to the problem,and various payloads of confidential data were hidden in medical images.The architectures of medical image steganalysis networks were transferred separately from eleven Dense Convolutional Network(DenseNet),Residual Neural Network(ResNet),and Inception-based models.The steganalysis outputs of these networks were determined by assembling models separately for each spatial embedding method with different payload ratios.The study demonstrated the success of pre-trained ResNet,DenseNet,and Inception models in the cover-stego mismatch scenario for each hiding technique with different payloads.Due to the high detection accuracy achieved,the proposed model has the potential to lead to the development of novel medical image steganography algorithms that existing deep learning-based steganalysis methods cannot detect.The experiments and the evaluations clearly proved this attempt.

Binary Image Steganalysis Based on Distortion Level Co-Occurrence Matrix

In recent years,binary image steganography has developed so rapidly that the research of binary image steganalysis becomes more important for information security.In most state-of-the-art binary image steganographic schemes,they always find out the flippable pixels to minimize the embedding distortions.For this reason,the stego images generated by the previous schemes maintain visual quality and it is hard for steganalyzer to capture the embedding trace in spacial domain.However,the distortion maps can be calculated for cover and stego images and the difference between them is significant.In this paper,a novel binary image steganalytic scheme is proposed,which is based on distortion level co-occurrence matrix.The proposed scheme first generates the corresponding distortion maps for cover and stego images.Then the co-occurrence matrix is constructed on the distortion level maps to represent the features of cover and stego images.Finally,support vector machine,based on the gaussian kernel,is used to classify the features.Compared with the prior steganalytic methods,experimental results demonstrate that the proposed scheme can effectively detect stego images.

Image Steganalysis System optimization Based on Boundary Samples

In the image steganalysis,the training samples often determine the performance of the model when the features and classification are in the same condition.However the existing research on steganalysis lacks the in-depth study of the classifier's training method which may deeply influence the detection performance.This paper provides an optimization of universal steganalysis based on the boundary samples classification concerning about image steganalysis.This paper proposes a strategy of selecting boundary samples in steganalysis and divides the training samples into good samples,poor samples and boundary samples three categories and then chose the optimal threshold to get boundary samples through experiments.The experimental results show the effectiveness of boundary sample,which dramatically improve detection capability especially for the low embedding rate Stego-image.

Multi-resolution network based image steganalysis model

Recently, many steganalysis approaches improve their feature extraction ability through addingconvolutional layers. However, it often leads to a decrease of resolution in the feature map during downsampling,which makes it challenging to extract weak steganographic signals accurately. To address this issue, this paperproposes a multi-resolution steganalysis net (MRS-Net). MRS-Net adopts a multi-resolution network to extract globalimage information, fusing the output feature map to ensure high-dimensional semantic information andsupplementing low-level detail information. Furthermore, the model incorporates an attention module which cananalyze image sensitivity based on different channel and spatial information, thus effectively focusing on areas withrich steganographic signals. Multiple benchmark experiments on the BOSSBase 1.01 dataset demonstrate that theaccuracy of MRS-Net significantly improves by 9.9% and 3.3% compared with YeNet and SRNet, respectively,demonstrating its exceptional steganalysis capability.

Passive Steganalysis Based on Higher Order Image Statistics of Curvelet Transform

Steganographic techniques accomplish covert communication by embedding secret messages into innocuous digital images in ways that are imperceptible to the human eye. This paper presents a novel passive steganalysis strategy in which the task is approached as a pattern classification problem. A critical part of the steganalyser design depends on the selection of informative features. This paper is aimed at proposing a novel attack with improved performance indices with the following implications： 1） employing higher order statistics from a curvelet sub-band image representation that offers better discrimination ability for detecting stego anomalies in images, as compared to other conventional wavelet transforms; 2） increasing the sensitivity and specificity of the system by the feature reduction phase; 3） realizing the system using an efficient classification engine, a neuro-C4.5 classifier, which provides better classification rate. An extensive experimental evaluation on a database containing 5600 clean and stego images shows that the proposed scheme is a state-of-the-art steganalyser that outperforms other previous steganalytic methods.

A Deep Learning Driven Feature Based Steganalysis Approach

The goal of steganalysis is to detect whether the cover carries the secret information which is embedded by steganographic algorithms.The traditional ste-ganalysis detector is trained on the stego images created by a certain type of ste-ganographic algorithm,whose detection performance drops rapidly when it is applied to detect another type of steganographic algorithm.This phenomenon is called as steganographic algorithm mismatch in steganalysis.To resolve this pro-blem,we propose a deep learning driven feature-based approach.An advanced steganalysis neural network is used to extract steganographic features,different pairs of training images embedded with steganographic algorithms can obtain diverse features of each algorithm.Then a multi-classifier implemented as lightgbm is used to predict the matching algorithm.Experimental results on four types of JPEG steganographic algorithms prove that the proposed method can improve the detection accuracy in the scenario of steganographic algorithm mismatch.

A Novel Universal Steganalysis Algorithm Based on the IQM and the SRM

The state-of-the-art universal steganalysis method,spatial rich model(SRM),and the steganalysis method using image quality metrics(IQM)are both based on image residuals,while they use 34671 and 10 features respectively.This paper proposes a novel steganalysis scheme that combines their advantages in two ways.First,filters used in the IQM are designed according to the models of the SRM owning to their strong abilities for detecting the content adaptive steganographic methods.In addition,a total variant(TV)filter is also used due to its good performance of preserving image edge properties during filtering.Second,due to each type of these filters having own advantages,the multiple filters are used simultaneously and the features extracted from their outputs are combined together.The whole steganalysis procedure is removing steganographic noise using those filters,then measuring the distances between images and their filtered version with the image quality metrics,and last feeding these metrics as features to build a steganalyzer using either an ensemble classifier or a support vector machine.The scheme can work in two modes,the single filter mode using 9 features,and the multi-filter mode using 639 features.We compared the performance of the proposed method,the SRM and the maxSRMd2.The maxSRMd2 is the improved version of the SRM.The simulated results show that the proposed method that worked in the multi-filter mode was about 10%more accurate than the SRM and maxSRMd2 when the data were globally normalized,and had similar performance with the SRM and maxSRMd2 when the data were locally normalized.

An Efficient Steganalysis Model Based on Multi-Scale LTP and Derivative Filters

Local binary pattern(LBP)is one of the most advanced image classification recognition operators and is commonly used in texture detection area.Research indicates that LBP also has a good application prospect in steganalysis.However,the existing LBP-based steganalysis algorithms are only capable to detect the least significant bit(LSB)and the least significant bit matching(LSBM)algorithms.To solve this problem,this paper proposes a steganalysis model called msdeLTP,which is based on multi-scale local ternary patterns(LTP)and derivative filters.The main characteristics of the msdeLTP are as follows:First,to reduce the interference of image content on features,the msdeLTP uses derivative filters to acquire residual images on which subsequent operations are based.Second,instead of LBP features,LTP features are extracted considering that the LTP feature can exhibit multiple variations in the relationship of adjacent pixels.Third,LTP features with multiple scales and modes are combined to show the relationship of neighbor pixels within different radius and along different directions.Analysis and simulation show that the msdeLTP uses only 2592-dimensional features and has similar detection accuracy as the spatial rich model(SRM)at the same time,showing the high steganalysis efficiency of the method.

A Method to Estimate the Steganographic Capacity in DCT Domain Based on MCUU Model

In order to estimate maximum steganographic capacity of discrete cosine transform（DCT） domain in JPEG image, this paper presents a method based on the maximize capacity under undetectable model（MCUU）. We analyze the relation between steganographic capacity and affecting factors（image size, steganography operator, loading band, embedding intensity and image complexity）. Then we design a steganography analyzer architecture and a steganographic algorithm which can dynamically increase the steganographic capacity. Compared with other methods of embedding capacity estimation in DCT domain, the proposed methods utilizes general steganalysis methods rather than one specific steganalysis technique and takes five essential factors into account, which improves the commonality and comprehensiveness of capacity estimation, respectively. The experimental results show that steganographic capacity for quantization index modulation（QIM） is almost twice that of spread spectrum（SS） based on MCUU model.