Color Image Steganalysis Based on Residuals of Channel Differences

Kang, Yuhan; Liu, Fenlin; Yang, Ching-Nung; Luo, Xiangyang; Zhang, Tingting

doi:10.32604/cmc.2019.05242

Cited by 18 publications

(14 citation statements)

References 19 publications

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“…For detection resistant image steganography techniques, current adaptive steganography algorithms, such as Highly Undetectable steGO (HUGO) steganography [11], Wavelet Obtained Weights (WOW) steganography [6], MiPOD (Minimizing the Power of Optimal Detector) [14], JPEG UNIversal Wavelet Relative Distortion (J-UNIWARD) steganography [7] and other algorithms [5], have become a research priority in the field of information hiding techniques. Utiliz-ing the appropriately defined distortion functions and minimizing embedding cost codes --STCs (Syndrome-Trellis Codes) [4], these algorithms can adaptively select embedding locations according to the content of cover images, thus realizing message embedding with a good detection resistance against steganalysis based on statistical features [8]. However, these algorithms usually do not consider the situation when the stego images are attacked during the transmission through public lossy channels exposed to image processing attacks, resulting in the embedded messages hard to survive after these attacks and the failure of covert communication under lossy channels [20].…”

Section: Introductionmentioning

confidence: 99%

Enhancing reliability and efficiency for real-time robust adaptive steganography using cyclic redundancy check codes

Zhang¹,

Luo²,

Zhu³

et al. 2019

J Real-Time Image Proc

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The development of multimedia and deep learning technology bring new challenges to steganography and steganalysis techniques. Meanwhile, robust steganography, as a class of new techniques aiming to solve the problem of covert communication under lossy channels, has become a new research hotspot in the field of information hiding. To improve the communication reliability and efficiency for current real-time robust steganography methods, a concatenated code, composed of Syndrome-Trellis Codes (STC) and Cyclic Redundancy Check (CRC) codes, is proposed in this manuscript. Using its strong error detection capability, high coding efficiency and low embedding costs, an enhanced robust adaptive steganography framework and three adaptive steganographic methods resisting JPEG compression and detection are proposed. On this basis, to provide a theoretical reference for message extraction integrity, the fault tolerance of the proposed steganography methods is analyzed using the residual model of JPEG compression, thus obtaining the appropriate coding parameters. Experimental results show that the proposed methods have a significantly stronger robustness against compression, and are more difficult to be detected by statistical based steganalytic methods comparing with existing robust steganography methods.

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Section: Introductionmentioning

confidence: 99%

Enhancing reliability and efficiency for real-time robust adaptive steganography using cyclic redundancy check codes

Zhang¹,

Luo²,

Zhu³

et al. 2019

J Real-Time Image Proc

Self Cite

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“…In the color image staganalysis the features are independently extracted from the residuals of each color channel, and then combined to form the feature vector in order to have better detection performance [19] [20]. The steganalsis is in another way of tempering, taking advantage of it, we applied the residuals of color differences for splicing localization.…”

Section: A Residuals Based Color Channel Differencesmentioning

confidence: 99%

“…Then for each superpixel, the color channels are seperated and obtained the residual superpixels of channel differences. In color image steganalysis, the high-dimensional rich model features are extracted from the residuals of the channel differences [19]. On each residual image we applied high-pass filter to suppress the unnecessary values and the noise level is estimated.…”

Section: Image Splicing Localizationmentioning

confidence: 99%

Splicing Localization Based on Noise Level Inconsistencies in Residuals of Color Channel Differences

Sekhar¹,

Shankar²

2019

IJRTE

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With the rapid usage of networking sites there is an enormous increase in image sharing over the internet. At the same time altering or tempering images has become much easier with the availability of photo editing software. Splicing is one of the tempering method, where an object from one image is copied and pasted into another image, is often used with the aim of either getting attention for fun or misleading the general masses. Thus, authenticity of images shared on internet is debatable. Active research is going on in the field of image forensics in order to examine the trustworthiness of the images. Amongst several techniques available for dealing with image splicing, the statistical based methods are gaining attention in research community as it uses image’s local statistics. We propose a simple and effective method based on noise inconsistencies in residuals of Color channel difference for forensic analysis to localize the splicing image forgery. First the image is decomposed in to super pixels and extracted in regular shapes. From each super pixel, three color channel differences are extracted and noise level is estimated on the residual. Finally, the super pixels are clustered into two groups using Farthest Distributed Centroids Clustering (FDCC) method for classifying superpixel as tampered or original. The experimental results show the simplicity and effectiveness of the proposed method over the state of the art.

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“…Steganalysis [1] and information hiding are mutually restricted and mutually promoted [2,3]. And there is a more hopeful prospect to carry out the steganalysis work.…”

Section: Introductionmentioning

confidence: 99%

Joint multi-domain feature learning for image steganalysis based on CNN

Wang

Chen

et al. 2020

J Image Video Proc.

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In recent years, researchers have been making great progress in the steganalysis technology based on convolution neural networks (CNN). However, experts ignore the contribution of nonlinear residual and joint domain detection to steganalysis, and how to detect the adaptive steganographic algorithms with low embedding rates is still challenging. In this paper, we propose a CNN steganalysis model that uses a joint domain detection mechanism and a nonlinear detection mechanism. For the nonlinear detection mechanism, based on the spatial rich model (SRM), we introduce the maximum and minimum nonlinear residual feature acquisition method into the model to adapt to the nonlinear distribution of steganography information. For the joint domain detection mechanism, we not only apply the high-pass filters from the SRM for spatial residuals, but also apply the patterns from the discrete cosine transform residual (DCTR) for transformation steganographic impacts, so as to fully capture the interference trace of spatial steganography to transform domain. We also apply a new transfer learning method to improve the model's performance. That is, we apply the low embedding rate steganography samples to initialize the model, because we think that the method makes the network more sensitive than applying high embedding rate steganography samples to initialize the model. The simulation results also confirm this assumption. Combined with the above improved methods, the detection accuracy of the model for WOW and S-UNIWARD is higher than that of SRM+EC, Ye-Net, Xu-Net, Yedroudj-Net and Zhu-Net, which is about 4∼6% higher than that of the optimal Zhu-Net. The results can provide a certain reference for steganalysis and image forensics tasks.

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Color Image Steganalysis Based on Residuals of Channel Differences

Cited by 18 publications

References 19 publications

Enhancing reliability and efficiency for real-time robust adaptive steganography using cyclic redundancy check codes

Enhancing reliability and efficiency for real-time robust adaptive steganography using cyclic redundancy check codes

Splicing Localization Based on Noise Level Inconsistencies in Residuals of Color Channel Differences

Joint multi-domain feature learning for image steganalysis based on CNN

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