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| 1 | Image universal steganalysis based on best wavelet packet decomposition显示文摘Based on the best wavelet packet decomposition of images, a new universal steganalysis method with high detection correct ratio is proposed. First, the best wavelet packet decomposition of image based on the Shannon entropy information cost function is made. Second, high order absolute characteristic function moments of histogram extracted from the coefficient subbands obtained by best wavelet packet decomposition are regarded as features. Finally, these features are processed and a back-propagation (BP) neural network is designed to classify original and stego images. Three different steganalysis algorithms for three different cases of background and application condition are presented. To validate the performance of the proposed method, a series of experiments are made for six kinds of typical steganography methods, i.e. LSB, LTSB, PMK, Jsteg, F5 and JPHide. Results show that, the average detection accuracy of the proposed method exceeds at least 6.4% and up to 15.4% and has a better universal performance than its closest competitors. Furthermore, the proposed method can provide reference for designing the pattern recognition and classification algorithm based on best wavelet packet decomposition. | LUO XiangYang LIU FenLin YANG ChunFang WANG DaoShun | 2010 | Science China(Information Sciences)2010，53，3： | 9 |
| 2 | Modification ratio estimation for a category of adaptive steganography显示文摘This paper investigates the detection of adaptive image steganography. Firstly, the sample pair analysis model of multiple least-significant bits (MLSB) replacement is analyzed, and the conditions under which the adaptive steganography can be detected via this model are given. For the category of adaptive steganography satisfying these conditions, a general quantitative steganalysis method is presented based on specific areas and sample pair analysis. Then, for a typical adaptive steganography, some concrete methods are proposed to select specific areas and trace sets of sample pairs, and estimate the stego modification ratios. Experimental results show that the proposed methods can estimate the stego modification ratio accurately. This verifies the validity of the proposed general quantitative steganalysis method. | LUO XiangYang LIU FenLin YANG ChunFang LIAN ShiGuo | 2010 | Science China(Information Sciences)2010，53，12： | 2 |
| 3 | Stepwise inter-frame correlation-based steganalysis system for video streams 显示文摘 | Liu Bin Liu Fenlin Yang Chenfang | 2008 | Security and Communication networks2008，1，6： | 1 |
| 4 | Extracting hidden messages of MLSB steganography based on optimal stego subset显示文摘Dear editor,In recent years, steganalysis researchers have tried their best to extract the hidden messages. For example, under the condition of a known embedding position generator, Liu et al.[1] proposed a collision attack algorithm to recover the stego key of least significant bit (LSB) steganography. Fridrich et al.[2] proposed a chi-squared-test-based method to recover the stego key of LSB steganography for the case of an unknown carrier. Under the condition of multiple stego images embedded into the same positions, Ker [3] first proposed locating | Chunfang YANG Xiangyang LUO Jicang LU Fenlin LIU | 2018 | Science China(Information Sciences)2018，61，11： | 1 |
| 5 | Extracting embedded messages using adaptive steganography based on optimal syndrome-trellis decoding paths显示文摘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. | Jialin Li Xiangyang Luo Yi Zhang Pei Zhang Chunfang Yang Fenlin Liu | 2022 | Digital Communications and Networks2022，8，4： | 1 |
| 6 | Homotopy method for a mean curvature-based denoising model显示文摘 | Yang Fenlin Chen Ke Yu Bo | 2012 | Journal of AppLied Numerical Mathematics2012，62，3： | 1 |
| 7 | A relaxed fixed point method for a mean curvature-based denoising model显示文摘 | Yang Fenlin Chen Ke Yu Bo | 2014 | Optimization Methods and Software2014，29，2： | 1 |
| 8 | Efficient homotopy solution and a convex combination of ROF and LLT models for imagerestoration 显示文摘 | Yang Fenlin Chen Ke Yu Bo | 2012 | International Journal of Numerical Analysis and Modeling2012，9，90： | 1 |
| 9 | Color Image Steganalysis Based on Residuals of Channel Differences显示文摘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%. | Yuhan Kang Fenlin Liu Chunfang Yang Xiangyang Luo Tingting Zhang | 2019 | Computers, Materials & Continua2019，，4： | 0 |
| 10 | Image Steganalysis Based on Deep Content Features Clustering显示文摘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. | Chengyu Mo Fenlin Liu Ma Zhu Gengcong Yan Baojun Qi Chunfang Yang | 2023 | Computers, Materials & Continua2023，76，9： | 0 |
| 11 | Automatic Mining of Security-Sensitive Functions from Source Code显示文摘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. | Lin Chen Chunfang Yang Fenlin Liu Daofu Gong Shichang Ding | 2018 | Computers, Materials & Continua2018，，8： | 0 |
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