Image Steganalysis Based on Deep Content Features Clustering

The training images with obviously different contents to the detected images will make the steganalysis model perform poorly in deep steganalysis.The existing methods try to reduce this effect by discarding some features related to image contents.Inevitably,this should lose much helpful information and cause low detection accuracy.This paper proposes an image steganalysis method based on deep content features clustering to solve this problem.Firstly,the wavelet transform is used to remove the high-frequency noise of the image,and the deep convolutional neural network is used to extract the content features of the low-frequency information of the image.Then,the extracted features are clustered to obtain the corresponding class labels to achieve sample pre-classification.Finally,the steganalysis network is trained separately using samples in each subclass to achieve more reliable steganalysis.We experimented on publicly available combined datasets of Bossbase1.01,Bows2,and ALASKA#2 with a quality factor of 75.The accuracy of our proposed pre-classification scheme can improve the detection accuracy by 4.84%for Joint Photographic Experts Group UNIversal WAvelet Relative Distortion(J-UNIWARD)at the payload of 0.4 bits per non-zero alternating current discrete cosine transform coefficient(bpnzAC).Furthermore,at the payload of 0.2 bpnzAC,the improvement effect is minimal but also reaches 1.39%.Compared with the previous steganalysis based on deep learning,this method considers the differences between the training contents.It selects the proper detector for the image to be detected.Experimental results show that the pre-classification scheme can effectively obtain image subclasses with certain similarities and better ensure the consistency of training and testing images.The above measures reduce the impact of sample content inconsistency on the steganalysis network and improve the accuracy of steganalysis.

Extracting embedded messages using adaptive steganography based on optimal syndrome-trellis decoding paths

Privacy protection is the key to maintaining the Internet of Things(IoT)communication strategy.Steganography is an important way to achieve covert communication that protects user data privacy.Steganalysis technology is the key to checking steganography security,and its ultimate goal is to extract embedded messages.Existing methods cannot extract under known cover images.To this end,this paper proposes a method of extracting embedded messages under known cover images.First,the syndrome-trellis encoding process is analyzed.Second,a decoding path in the syndrome trellis is obtained by using the stego sequence and a certain parity-check matrix,while the embedding process is simulated using the cover sequence and parity-check matrix.Since the decoding path obtained by the stego sequence and the correct parity-check matrix is optimal and has the least distortion,comparing the path consistency can quickly filter the coding parameters to determine the correct matrices,and embedded messages can be extracted correctly.The proposed method does not need to embed all possible messages for the second time,improving coding parameter recognition significantly.The experimental results show that the proposed method can identify syndrome-trellis coding parameters in stego images embedded by adaptive steganography quickly to realize embedded message extraction.

Color Image Steganalysis Based on Residuals of Channel Differences

This study proposes a color image steganalysis algorithm that extracts highdimensional rich model features from the residuals of channel differences.First,the advantages of features extracted from channel differences are analyzed,and it shown that features extracted in this manner should be able to detect color stego images more effectively.A steganalysis feature extraction method based on channel differences is then proposed,and used to improve two types of typical color image steganalysis features.The improved features are combined with existing color image steganalysis features,and the ensemble classifiers are trained to detect color stego images.The experimental results indicate that,for WOW and S-UNIWARD steganography,the improved features clearly decreased the average test errors of the existing features,and the average test errors of the proposed algorithm is smaller than those of the existing color image steganalysis algorithms.Specifically,when the payload is smaller than 0.2 bpc,the average test error decreases achieve 4%and 3%.

Automatic Mining of Security-Sensitive Functions from Source Code

When dealing with the large-scale program,many automatic vulnerability mining techniques encounter such problems as path explosion,state explosion,and low efficiency.Decomposition of large-scale programs based on safety-sensitive functions helps solve the above problems.And manual identification of security-sensitive functions is a tedious task,especially for the large-scale program.This study proposes a method to mine security-sensitive functions the arguments of which need to be checked before they are called.Two argument-checking identification algorithms are proposed based on the analysis of two implementations of argument checking.Based on these algorithms,security-sensitive functions are detected based on the ratio of invocation instances the arguments of which have been protected to the total number of instances.The results of experiments on three well-known open-source projects show that the proposed method can outperform competing methods in the literature.

ENTREPRENEURIAL OPPORTUNITY,VALUE EVOLUTION,AND ORGANIZATIONAL INTEGRATION OF PROFESSIONAL FARMER ENTREPRENEURSHIP:A MULTI-CASE COMPARISON IN CHINA

A comparative multi-case analysis of professional farmer entrepreneurship cases in China was performed by applying the structural hole theory.The results confirmed four views.(1)Choosing the modern agriculture project entrepreneurship is rational for professional farmers,who return from urban,to reduce the interference from non-market factors.The success of this project stems from its ability to successfully occupy the structural hole of the market trading network.(2)In economically developed areas,professional farmers start their businesses and reduce transaction costs with factors by occupying‘self-benefit’or‘mutual-benefit’structural holes in market networks.(3)In traditional agricultural areas,for reducing factor transaction costs,professional farmers occupy the organizational-governance structural hole of rural social relationship networks and the mutual-benefit structural hole of market networks to start their businesses.(4)The embedding order of these two structural holes will change depending on the differences in the local resource endowment.This article proposes some suggestions to encourage professional farmers to develop featured agricultural projects,build a close benefit-linked mechanism with smallholders through the provision of socialized services,and participate in the governance of rural communities.

Titanium alloy composited with dual-cytokine releasing polysaccharide hydrogel to enhance osseointegration via osteogenic and macrophage polarization signaling pathways

Titanium alloy has been widely used in orthopedic surgeries as bone defect filling.However,the regeneration of high-quality new bones is limited due to the pro-inflammatory microenvironment around implants,resulting in a high occurrence rate of implant loosening or failure in osteological therapy.In this study,extracellular matrix-mimetic polysaccharide hydrogel co-delivering BMP-2 and interleukin(IL)-4 was composited with 3D printed titanium alloy to promote the osseointegration and regulate macrophage response to create a pro-healing microenvironment in bone defect.Notably,it is discovered from the bioinformatics data that IL-4 and BMP-2 could affect each other through multiple signal pathways to achieve a synergistic effect toward osteogenesis.The composite scaffold significantly promoted the osteoblast differentiation and proliferation of human bone marrow mesenchyme stem cells(hBMSCs).The repair of large-scale femur defect in rat indicated that the dual-cytokinedelivered composite scaffold could manipulate a lower inflammatory level in situ by polarizing macrophages to M2 phenotype,resulting in superior efficacy of mature new bone regeneration over the treatment of native titanium alloy or that with an individual cytokine.Collectively,this work highlights the importance of M2-type macrophages-enriched immune-environment in bone healing.The biomimetic hydrogel–metal implant composite is a versatile and advanced scaffold for accelerating in vivo bone regeneration,holding great promise in treating orthopedic diseases.