Locating secret messages based on quantitative steganalysis

Yang, Ching-Nung; Liu, Fenlin; Ge, Shuangkui; Lu, Jicang; Huang, Jie

doi:10.3934/mbe.2019247

Cited by 7 publications

(2 citation statements)

References 34 publications

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“…As the steganography techniques could be maliciously used for stealing confidential information, it is practically significant to carry on researches to forensics of the crime. For decades, researchers have proposed many techniques for the forensics of steganography, including the stego detection [2][3][4][5][6][7][8][9][10], the payload location [11][12][13][14][15], the embedding key restore [16,17], the secret message extraction [17], and the steganographer detection [18][19][20]. In practice, the covert communication entity on the Internet usually acts as the user of social platforms, whose location is virtual.…”

Section: Introductionmentioning

confidence: 99%

Geolocation of covert communication entity on the Internet for post-steganalysis

Zhang

Liu

Luo

2020

J Image Video Proc.

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Geolocation of covert communication entity is significantly important for the forensics of the crime but has significant challenges when the steganalyst locks the guilty actor IP and wants to know the physical location of the actor. This kind of post-steganalysis involves not only the stegos transmitted on the Internet but the IP package head and content. This paper presents a geolocation method for the location of the covert communication entity based on hophot path coding. The method estimates the location of the covert communication entity by combining the path and delay between probes and the covert communication entity IP, which improves the deficiency that similar delays do not necessarily mean close geographical locations of the IPs. Moreover, the similarity between the IPs' paths can be measured by coding the paths between IPs and probes. The results of a series of experiments show that the median error of the proposed method is within 6.16 km using different thresholds.

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Section: Introductionmentioning

confidence: 99%

Geolocation of covert communication entity on the Internet for post-steganalysis

Zhang

Liu

Luo

2020

J Image Video Proc.

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“…In 2014, Quach used the residuals to estimate the rough sequence of stego positions when owning enough stego images embedded messages of different sizes along the same path [18]. In 2019, Yang et al proposed a locating methodology based on quantitative steganalysis for this case [19]. Recently, Wang et al proposed a payload locating method based on co-frequency subimage filtering for a category of pseudo-random JPEG image steganography, such as JSteg and F5 steganography [20].…”

Section: Introductionmentioning

confidence: 99%

Stego key recovery method for F5 steganography with matrix encoding

Liu

Yang

Wang

et al. 2020

J Image Video Proc.

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When embedding secret message into image by steganography with matrix encoding, there are still no effective methods to recover the stego key because it is difficult to statistically distinguish the stego coefficient sequences selected by true and false keys. Therefore, this paper proposes a method for recovering the stego key of a typical JPEG (Joint Photographic Experts Group) image steganography—F5 which composes of the check matrix and shuffling key. Firstly, the check matrix is recovered based on the embedding ratio estimated by quantitative steganalysis. The shuffling key is then recovered based on the distribution difference between the bit sequences extracted by the true and false shuffling keys. Additionally, the cardinality of the shuffling key space is significantly reduced by examining the extracted encoding parameter and message length. Experimental results show that the proposed method can recover the stego key accurately and efficiently, even when the existing Xu’s method fails for the high or very low embedding ratio.

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