Color image steganalysis based on embedding change probabilities in differential channels

Yang, Ching-Nung; Kang, Yuhan; Liu, Fenlin; Song, Xiaocheng; Wang, Jie; Luo, Xiangyang

doi:10.1177/1550147720917826

Cited by 9 publications

(3 citation statements)

References 26 publications

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“…Recently, many researchers developed analytical techniques to extract crucial hidden information from the stego-image. In this work, the proposed hiding technique works against visual and statistical attacks ( Kang et al, 2019 ; Yang et al, 2020 ; Jin et al, 2020 ). The main challenge in steganography algorithms is balancing between the size of the secret message (SM) that is embedded in the cover image (CI) and the quality of the stego-image (SI).…”

Section: Introductionmentioning

confidence: 99%

New deep data hiding and extraction algorithm using multi-channel with multi-level to improve data security and payload capacity

Hardan¹,

Alawneh²,

El-Emam³

2022

PeerJ Computer Science

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The main challenge in steganography algorithms is balancing between the size of the secret message (SM) that is embedded in the cover image (CI) and the quality of the stego-image (SI). This manuscript proposes a new steganography algorithm to hide a large amount of secret messages in cover images with a high degree of non-perception in the resulting images. The proposed algorithm applied a multi-channel deep data hiding and extraction algorithm (MCDHEA) based on a modified multi-level steganography (MLS) approach. This approach used a new modification of the least significant bits (NMLSB) to make it hard to extract a secret message from attackers. The secret message was distributed among n-blocks; each block was hidden into a sub-channel that included multi-level hiding and flows into the main channel. Different grayscale images were used through the levels of each sub-channel and using the color image in the last level of the main channel. The image size of the multi-level was expanded from one level to the next level, and at each level, lossless image compression using the Huffman coding algorithm was applied to enable the size of the data hiding at the next level. In addition, the encryption of secret messages and intermediate cover images based on the XOR encryption algorithm is applied before the hiding process. Finally, the number of bits to be replaced at each level for both sub and main channels was four bits per byte except at the last level of the main channel based on a new approach using a non-uniform number of bits replacements. This algorithm’s performance was evaluated using various measures. The results show that the proposed technique is effective and better than the previous works concerning imperceptibility and robustness. Furthermore, the results show that the maximum peak signal-to-noise ratio (PSNR) of 61.2 dB for the payload of 18,750 bytes, the maximum video information fidelity (VIF) of 0.95 for the payload of 19,660 bytes, and the maximum structural similarity index measure (SSIM) of 0.999 for the payload of 294,912 bytes.

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Section: Introductionmentioning

confidence: 99%

New deep data hiding and extraction algorithm using multi-channel with multi-level to improve data security and payload capacity

Hardan¹,

Alawneh²,

El-Emam³

2022

PeerJ Computer Science

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“…Note that all above mentioned methods are designed for detecting grayscale stegos, and they are not very suitable for detecting color stegos generated by some steganography methods, such as [22]- [27]. Recently, several traditional steganalytic methods, such as [28]- [31], have been proposed for color images. For instance, Goljan et al [30] proposed an extension of the SRM [7] for steganalysis of color images (called CRM).…”

mentioning

confidence: 99%

“…For instance, Goljan et al [30] proposed an extension of the SRM [7] for steganalysis of color images (called CRM). Yang et al [31] presented a variant of SRM feature based on the embedding change probabilities in differential channels between different color channels. In spatial domain, Zeng et al [32] first design a wider separatethen-reunion network (called WISERNet) for steganalysis of color images, and achieve better results compared with related methods based on handcrafted features.…”

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confidence: 99%

Universal Deep Network for Steganalysis of Color Image based on Channel Representation

Wei¹,

Luo²,

Tan³

et al. 2021

Preprint

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Up to now, most existing steganalytic methods are designed for grayscale images, and they are not suitable for color images that are widely used in current social networks. In this paper, we design a universal color image steganalysis network (called UCNet) in spatial and JPEG domains. The proposed method includes preprocessing, convolutional, and classification modules. To preserve the steganographic artifacts in each color channel, in preprocessing module, we firstly separate the input image into three channels according to the corresponding embedding spaces (i.e. RGB for spatial steganography and YCbCr for JPEG steganography), and then extract the image residuals with 62 fixed high-pass filters, finally concatenate all truncated residuals for subsequent analysis rather than adding them together with normal convolution like existing CNN-based steganalyzers. To accelerate the network convergence and effectively reduce the number of parameters, in convolutional module, we carefully design three types of layers with different shortcut connections and group convolution structures to further learn high-level steganalytic features. In classification module, we employ a global average pooling and fully connected layer for classification. We conduct extensive experiments on ALASKA II to demonstrate that the proposed method can achieve state-of-the-art results compared with the modern CNN-based steganalyzers (e.g., SRNet and J-YeNet) in both spatial and JPEG domains, while keeping relatively few memory requirements and training time. Furthermore, we also provide necessary descriptions and many ablation experiments to verify the rationality of the network design.

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Novel color image steganalysis method based on RGB channel empirical modes to expose stego images with diverse payloads

Amrutha¹,

Arivazhagan²,

Jebarani³

2022

Pattern Anal Applic

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Color image steganalysis based on embedding change probabilities in differential channels

Cited by 9 publications

References 26 publications

New deep data hiding and extraction algorithm using multi-channel with multi-level to improve data security and payload capacity

New deep data hiding and extraction algorithm using multi-channel with multi-level to improve data security and payload capacity

Universal Deep Network for Steganalysis of Color Image based on Channel Representation

Novel color image steganalysis method based on RGB channel empirical modes to expose stego images with diverse payloads

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