Ensemble of CNNs for Steganalysis

Xu, Guanshuo; Wu, Hanzhou; Shi, Yun Q.

doi:10.1145/2909827.2930798

Cited by 132 publications

(58 citation statements)

References 19 publications

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“…Current published works on applying convolutional neural networks (CNNs) to image steganalysis mainly focus on detecting steganography embedding in the original spatial domain [1][2][3][4][5][6]. Studies on applying CNNs for JPEG steganalysis have not been extensively carried out even though JPEG steganography could be more conveniently used in practice.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network to Detect J-UNIWARD

Xu¹

2017

Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security

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This paper presents an empirical study on applying convolutional neural networks (CNNs) to detecting J-UNIWARDone of the most secure JPEG steganographic method. Experiments guiding the architectural design of the CNNs have been conducted on the JPEG compressed BOSSBase containing 10,000 covers of size 512×512. Results have verified that both the pooling method and the depth of the CNNs are critical for performance. Results have also proved that a 20-layer CNN, in general, outperforms the most sophisticated feature-based methods, but its advantage gradually diminishes on hard-to-detect cases. To show that the performance generalizes to large-scale databases and to different cover sizes, one experiment has been conducted on the CLS-LOC dataset of ImageNet containing more than one million covers cropped to unified size of 256×256. The proposed 20-layer CNN has cut the error achieved by a CNN recently proposed for large-scale JPEG steganalysis by 35%. Source code is available via GitHub: https://github.com/GuanshuoXu/deep_cnn_jpeg_steganalysis

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Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network to Detect J-UNIWARD

Xu¹

2017

Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security

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203

154

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“…In Table 2, the 5th column shows the experimental results of our proposed network with original images, and the 6th-8th columns show the performance of all of the compared methods. We can find that in general our network achieves better performance than the method in [14] and SRM [7]. When the embedding rates are high, the detection accuracy is higher than that of the state-of-the-art method maxSRMd2 [10] except for detecting WOW [3].…”

Section: The Effectiveness Of the Proposed Networkmentioning

confidence: 75%

“…We compare our method with the single network proposed in [14], SRM [7], and maxSRMd2 [10], and the corresponding experimental results are shown in columns 6-8. The best detection result of our proposed method in each row is shown in bold font.…”

Section: Results and Analysismentioning

confidence: 99%

“…We would like to use Figure 2 shows the network model designed in this paper, including preprocessing module, the convolution and downsampling module, and classification module. Our method broadens the network proposed in [14] with three separate subnets and lengthens the network with "depthconcat" layers, fully connected layers, and dropout layers. In the preprocessing module, the high-pass filter layer uses the KV filter kernel [7,12], "SQUARE5x5," to obtain the image residuals.…”

Section: The Region Selectionmentioning

confidence: 99%

“…Qian et al [12] proposed a CNN based steganalysis method using Gaussian activation function and 2 Security and Communication Networks in [13] they transfer features generated from a pretrained model to regularize CNN model. In literatures [14,15], Xu et al proposed a new-type network structure, including absolute value layer and batch normalization layer. In [16,17], hybrid networks composing many subnetworks are designed to fit the rich-model features set.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Steganalysis Based on Selection Region and Combined Convolutional Neural Networks

Shen

Zhou

et al. 2017

Security and Communication Networks

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Digital image steganalysis is the art of detecting the presence of information hiding in carrier images. When detecting recently developed adaptive image steganography methods, state-of-art steganalysis methods cannot achieve satisfactory detection accuracy, because the adaptive steganography methods can adaptively embed information into regions with rich textures via the guidance of distortion function and thus make the effective steganalysis features hard to be extracted. Inspired by the promising success which convolutional neural network (CNN) has achieved in the fields of digital image analysis, increasing researchers are devoted to designing CNN based steganalysis methods. But as for detecting adaptive steganography methods, the results achieved by CNN based methods are still far from expected. In this paper, we propose a hybrid approach by designing a region selection method and a new CNN framework. In order to make the CNN focus on the regions with complex textures, we design a region selection method by finding a region with the maximal sum of the embedding probabilities. To evolve more diverse and effective steganalysis features, we design a new CNN framework consisting of three separate subnets with independent structure and configuration parameters and then merge and split the three subnets repeatedly. Experimental results indicate that our approach can lead to performance improvement in detecting adaptive steganography.

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