Steganalysis of HUGO steganography based on parameter recognition of syndrome-trellis-codes

Luo, Xiangyang; Song, Xiaocheng; Li, Xiaolong; Zhang, Weiming; Lu, Jicang; Yang, Ching-Nung; Liu, Fenlin

doi:10.1007/s11042-015-2759-2

Cited by 70 publications

(29 citation statements)

References 21 publications

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“…In addition, related anti-steganalysis algorithms based on statistical analysis can detect the existence of secret information. Reference [13] proposed a steganalysis method aimed at HUGO steganography, which can detect the stego-images reliably, and extract the embedded message as well. Reference [14] proposed a general steganalysis feature selection method based on decision rough set α-positive region reduction, improving the efficiency of steganalysis algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…For the proposed algorithm, the stego image and cover image are exactly the same; therefore, the stego image has the highest PSNR because of no modifications in the cover image. As a result, existing steganalysis techniques [13,14] fail to detect the secret information. Through the procedure of the covert communication, there is no auxiliary information generated, which reduces the skepticism and suspicion of a latent adversary.…”

Section: Security Analysismentioning

confidence: 99%

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A Coverless Information Hiding Algorithm Based on Grayscale Gradient Co-occurrence Matrix

Liu

Dai

et al. 2018

IETE Technical Review

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In this paper, a coverless information hiding algorithm is introduced. In which, the grayscale gradient co-occurrence matrix is used to encode images and the mapping relationship between the images and the random numbers is used to express the payload information. There are three steps for this algorithm. Firstly, the grayscale gradient co-occurrence matrix of cover image is calculated, in accordance with which a descriptor is introduced. Secondly, the descriptor is quantized into a binary sequence to construct a mapping relationship between the cover image and the binary random numbers. Finally, the binary secret information sequence is divided into many segments, and the correct images corresponding to those segments are selected from the image database according to the mapping relationship. Moreover, the secret information is encrypted by Turbo encoder to improve the security. The experimental results show that the proposed algorithm has a good tolerance towards JPEG compression attack and low-pass filter attack. This promising algorithm which can be applied into remote sensing satellites leads an applied value in covert communication with high-security.

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Section: Related Workmentioning

confidence: 99%

Section: Security Analysismentioning

confidence: 99%

A Coverless Information Hiding Algorithm Based on Grayscale Gradient Co-occurrence Matrix

Liu

Dai

et al. 2018

IETE Technical Review

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“…CNN allows to directly inputting the individual character images to be recognized without feature extraction, and it has a certain degree of robustness in displacement, scale, and deformation. As a deep learning network, CNN has been widely used in the fields of CAPTCHA recognition [7,12,13], information hiding [18,19,20,21], and so forth.…”

Section: Recognitionmentioning

confidence: 99%

An Attack on Hollow CAPTCHA Using Accurate Filling and Nonredundant Merging

Luo¹,

Hu²,

et al. 2018

IETE Technical Review

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As one of the effective access control mechanism, CAPTCHA can provide privacy protection and multimedia security for big data. In this paper, an attack on hollow CAPTCHA using accurate filling and nonredundant merging is proposed. Firstly, a thinning operation is introduced to repair character contour precisely. Secondly, an inner-outer contour filling algorithm is presented to acquire solid characters, which only fills the hollow character components rather than noise blocks. Thirdly, the segmentation to the solid characters produces mostly individual characters and only a small number of character components. Fourthly, a minimum-nearest neighbor merging algorithm is proposed to obtain individual characters without redundancy. Lastly, the convolutional neural network (CNN) is applied to acquire the final recognition results. The experimental results based on the real CAPTCHA data sets show that comparing with the existing attack methods on hollow CAPTCHA, the proposed method has higher success rate and superior efficiency.

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“…High-precision IP geolocation has a wide application prospect in protecting social network privacy [Wang, Zhang, Lu et al (2018)], locating covert communication subjects [Ma, Luo, Li et al (2018); Zhang, Qin, Zhang et al (2018); Luo, Song, Li et al (2016); Wang, Li, Luo et al (2018)], and supporting indoor localization ]. The landmark-based IP geolocation method is a commonly used and accurate geolocation method.…”

Section: Introductionmentioning

confidence: 99%

Street-Level Landmarks Acquisition Based on SVM Classifiers

Li¹,

Liu²,

Qiao³

et al. 2019

Computers, Materials &Amp; Continua

Self Cite

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High-density street-level reliable landmarks are one of the important foundations for street-level geolocation. However, the existing methods cannot obtain enough street-level landmarks in a short period of time. In this paper, a street-level landmarks acquisition method based on SVM (Support Vector Machine) classifiers is proposed. Firstly, the port detection results of IPs with known services are vectorized, and the vectorization results are used as an input of the SVM training. Then, the kernel function and penalty factor are adjusted for SVM classifiers training, and the optimal SVM classifiers are obtained. After that, the classifier sequence is constructed, and the IPs with unknown service are classified using the sequence. Finally, according to the domain name corresponding to the IP, the relationship between the classified server IP and organization name is established. The experimental results in Guangzhou and Wuhan city in China show that the proposed method can be as a supplement to existing typical methods since the number of obtained street-level landmarks is increased substantially, and the median geolocation error using evaluated landmarks is reduced by about 2 km.

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Steganalysis of HUGO steganography based on parameter recognition of syndrome-trellis-codes

Cited by 70 publications

References 21 publications

A Coverless Information Hiding Algorithm Based on Grayscale Gradient Co-occurrence Matrix

A Coverless Information Hiding Algorithm Based on Grayscale Gradient Co-occurrence Matrix

An Attack on Hollow CAPTCHA Using Accurate Filling and Nonredundant Merging

Street-Level Landmarks Acquisition Based on SVM Classifiers

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