Payload location for JPEG image steganography based on co-frequency sub-image filtering

Abstract

In digital steganography, due to difficulties estimating the JPEG cover image, it is still very hard to accurately locate the hidden message embedded in a JPEG image. Therefore, this study proposes a payload location method for a category of pseudo-random scrambled JPEG image steganography. In order to estimate the quantized discrete cosine transform coefficients in the cover JPEG image, a cover JPEG image estimation method is proposed based on co-frequency sub-image filtering. The proposed payload location method defines a general residual, uses the estimated cover JPEG image to compute the residuals, and then employs the mean residuals of multiple stego images embedded along the same path to distinguish the stego positions. The proposed cover JPEG image estimation method constructs 64 co-frequency sub-images, and then filters the sub-image to estimate the cover JPEG image. Finally, using these methods, payload location algorithms are designed for two common JPEG image steganography algorithms: JSteg and F5. Experimental results show that the proposed location algorithms can effectively locate the stego positions in both JSteg and F5 steganography when the investigator possesses multiple stego images embedded along the same path. In addition, the location results can also be used to recover the steganography key to extract the embedded secret messages.

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

“…Since the coefficients in the same position represent the magnitude of energy in the same frequency and the adjacent blocks in an image still have strong similarity, the coefficients in the same position of adjacent blocks still have a strong correlation. According to the property, this section will use the same method in [27] to divide the given JPEG images into 64 co-frequency sub-images, then use the maximum a posterior probability algorithm to estimate the optimal cover co-frequency sub-images, and combine them to get the optimal estimation of cover JPEG image.…”

“…Therefore, the number of stego images is very important for locating the stego positions. Figure 7 compares the accuracies of the proposed algorithm and the payload location algorithm based on co-frequency sub-image wavelet filtering (CSW-F5 ) [27]. The 1000 stego images are generated with the same embedding path and the embedding ratio of 0.5.…”

“…When the messages are embedded into the JPEG images, recently, the authors [27] proposed a payload location method based on co-frequency sub-image filtering for a category of pseudo-random scrambled JPEG image steganography. The accuracy of this payload location method is influenced by the fidelity of the estimated cover images and can be improved if a more precise estimator can be designed.…”

Feature Selection of the Rich Model Based on the Correlation of Feature Components

“…With the presentation of the HUGO steganography algorithm [7] in 2010, adaptive steganography based on the framework of "distortion function + STC coding" has become the mainstream of image steganography. Based on this framework, researchers have successively proposed a series of adaptive steganography algorithms with high antidetection performance, which make the traditional steganalysis algorithms mostly invalid [8][9][10][11][12]. In 2012, Fridrich and Kodovský proposed the Rich Model steganalysis feature [13], which effectively improved the detection performance for HUGO steganography.…”

Stego key recovery method for F5 steganography with matrix encoding

“…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]. (2) In the case of that the investigator owns a single stego image, in 2012, Quach proved that the modified pixels in a stego image can be located with a lower error rate if enough independent non-random discriminant functions can be used [21].…”