An Improved Steganalysis Method Using Feature Combinations

Wang, Zichi; Qian, Zhenxing; Zhang, Xinpeng; Li, Sheng

doi:10.1007/978-3-030-24271-8_11

Cited by 6 publications

(2 citation statements)

References 29 publications

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“…Accordingly, researchers have proposed many steganalysis approaches, such as early classical Chi-square Attack [9], block effect detection [10], and histogram estimation detection [11]. There are also subsequent high-dimensional feature detection methods such as Spatial Rich Model (SRM) [12], Discrete Cosine Transform Residual (DCTR) [13], Gabor Filter Residual (GFR) [14] and features combinations method [15]. In recent years, inspired by the excellent performance of deep learning in image classification, researchers have also introduced deep learning into steganalysis and proposed many excellent approaches.…”

Section: Introductionmentioning

confidence: 99%

Image Steganalysis Based on Deep Content Features Clustering

Mo,

Liu,

Zhu

et al. 2023

Computers, Materials &Amp; Continua

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The training images with obviously different contents to the detected images will make the steganalysis model perform poorly in deep steganalysis. The existing methods try to reduce this effect by discarding some features related to image contents. Inevitably, this should lose much helpful information and cause low detection accuracy. This paper proposes an image steganalysis method based on deep content features clustering to solve this problem. Firstly, the wavelet transform is used to remove the high-frequency noise of the image, and the deep convolutional neural network is used to extract the content features of the low-frequency information of the image. Then, the extracted features are clustered to obtain the corresponding class labels to achieve sample pre-classification. Finally, the steganalysis network is trained separately using samples in each subclass to achieve more reliable steganalysis. We experimented on publicly available combined datasets of Bossbase1.01, Bows2, and ALASKA#2 with a quality factor of 75. The accuracy of our proposed pre-classification scheme can improve the detection accuracy by 4.84% for Joint Photographic Experts Group UNIversal WAvelet Relative Distortion (J-UNIWARD) at the payload of 0.4 bits per non-zero alternating current discrete cosine transform coefficient (bpnzAC). Furthermore, at the payload of 0.2 bpnzAC, the improvement effect is minimal but also reaches 1.39%. Compared with the previous steganalysis based on deep learning, this method considers the differences between the training contents. It selects the proper detector for the image to be detected. Experimental results show that the pre-classification scheme can effectively obtain image subclasses with certain similarities and better ensure the consistency of training and testing images. The above measures reduce the impact of sample content inconsistency on the steganalysis network and improve the accuracy of steganalysis.

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Section: Introductionmentioning

confidence: 99%

Image Steganalysis Based on Deep Content Features Clustering

Mo,

Liu,

Zhu

et al. 2023

Computers, Materials &Amp; Continua

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“…In the recent studies of steganalysis, most researchers focus on detecting if the secret information is hidden in an image. Relying on the rich models, together with the ensemble learning-based mechanism, the state of the arts (see [19][20][21][22]) perform very well in dealing with the problem of classifying between cover and stego images. Recently, in the framework of deep learning [23,24], instead of hand-crafted feature extraction, the realization of end-to-end automatic image steganalysis gradually becomes widespread (see [25][26][27][28][29][30][31][32][33]).…”

Section: Introductionmentioning

confidence: 99%

Toward Steganographic Payload Location via Neighboring Weight Algorithm

Qiao

Luo

Pan

et al. 2022

Security and Communication Networks

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Modern steganalysis has been widely investigated, most of which mainly focus on dealing with the problem of detecting whether an inquiry image contains hidden information. However, few articles in the literature study the location of secret bits hidden by modern adaptive steganography. In this paper, we propose a novel algorithm for locating steganographic payload in the spatial domain. We first predict the steganographic scheme and its payload, which is used for generating a random bitstream. Then, the random bits are embedded in the stego image based on the cost matrix in the framework of Syndrome-Trellis Codes (STCs). Next, relying on the differences between two stego images, the extended modification map in couple with the neighboring weight algorithm can be acquired, leading to the location of the hidden bits. Compared with the prior art, the extensive experiments verify that our proposed locating algorithm performs better, in terms of locating accuracy and efficiency.

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On Improving Perceptual Image Hashing Using Reference Image Construction

Wang

2022

Lecture Notes in Electrical Engineering

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An Improved Steganalysis Method Using Feature Combinations

Cited by 6 publications

References 29 publications

Image Steganalysis Based on Deep Content Features Clustering

Image Steganalysis Based on Deep Content Features Clustering

Toward Steganographic Payload Location via Neighboring Weight Algorithm

On Improving Perceptual Image Hashing Using Reference Image Construction

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