Locating Steganalysis of LSB Matching Based on Spatial and Wavelet Filter Fusion

Yang, Ching-Nung; Wang, Jie; Lin, C.W.; Chen, Huiqin; Wang, Wenjuan

doi:10.32604/cmc.2019.06154

Cited by 6 publications

(4 citation statements)

References 17 publications

(27 reference statements)

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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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“…Although Quach [17] has proved the locatability of modified pixels in a single stego image, the actual steganography payload algorithms designed for a single stego image can only locate the steganography payload with low accuracy because it is very difficult to precisely estimate the cover of the given stego image and about half of the stego elements are still unchanged [18]. However, for the convenience of communication, many communication participants use the same key in a certain period of time and limit the embedding ratio.…”

Section: Introductionmentioning

confidence: 99%

JPEG image steganography payload location based on optimal estimation of cover co-frequency sub-image

Wang

Yang

Zhu

et al. 2021

J Image Video Proc.

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The excellent cover estimation is very important to the payload location of JPEG image steganography. But it is still hard to exactly estimate the quantized DCT coefficients in cover JPEG image. Therefore, this paper proposes a JPEG image steganography payload location method based on optimal estimation of cover co-frequency sub-image, which estimates the cover JPEG image based on the Markov model of co-frequency sub-image. The proposed method combines the coefficients of the same position in each 8 × 8 block in the JPEG image to obtain 64 co-frequency sub-images and then uses the maximum a posterior (MAP) probability algorithm to find the optimal estimations of cover co-frequency sub-images by the Markov model. Then, the residual of each DCT coefficient is obtained by computing the absolute difference between it and the estimated cover version of it, and the average residual over coefficients in the same position of multiple stego images embedded along the same path is used to estimate the stego position. The experimental results show that the proposed payload location method can significantly improve the locating accuracy of the stego positions in low frequencies.

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“…Usually, this task is formulated as a binary classification problem to distinguish between cover images and stego images. Compare with tradition SRM (Spatial Rich Model) [13], several deep learning steganalysis methods [14][15][16][17][18][19] have been proposed to solve the steganalysis problem which improves detection accuracy to a new level.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Security of Deep Learning Steganography via Adversarial Examples

Shang

Jiang

et al. 2020

Mathematics

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Steganography is a collection of techniques for concealing the existence of information by embedding it within a cover. With the development of deep learning, some novel steganography methods have appeared based on the autoencoder or generative adversarial networks. While the deep learning based steganography methods have the advantages of automatic generation and capacity, the security of the algorithm needs to improve. In this paper, we take advantage of the linear behavior of deep learning networks in higher space and propose a novel steganography scheme which enhances the security by adversarial example. The system is trained with different training settings on two datasets. The experiment results show that the proposed scheme could escape from deep learning steganalyzer detection. Besides, the produced stego could extract secret image with less distortion.

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Locating Steganalysis of LSB Matching Based on Spatial and Wavelet Filter Fusion

Cited by 6 publications

References 17 publications

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

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

JPEG image steganography payload location based on optimal estimation of cover co-frequency sub-image

Enhancing the Security of Deep Learning Steganography via Adversarial Examples

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