CAPTCHA recognition based on deep convolutional neural network

Wang, Jing; Qin, Jiao Hua; Xiang, Xu; Tan, Yun; Pan, Nan

doi:10.3934/mbe.2019292

Cited by 76 publications

(26 citation statements)

References 17 publications

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“…The application field of convolutional neural network is quite extensive, such as image recognition [18][19][20], image classification [21][22][23][24], target tracking [25][26][27][28], text analysis [29][30][31][32], target detection, and image retrieval [33,34]. It is a powerful tool for image processing and research.…”

Section: Semantic Segmentation Based On Convolutional Neural Networkmentioning

confidence: 99%

A video coverless information hiding algorithm based on semantic segmentation

Pan

Qin

Tan

et al. 2020

J Image Video Proc.

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Due to the fact that coverless information hiding can effectively resist the detection of steganalysis tools, it has attracted more attention in the field of information hiding. At present, most coverless information hiding schemes select text and image as transmission carriers, while there are few studies on emerging popular media such as video, which has more abundant contents. Taking the natural video as the carrier is more secure and can avoid the attention of attackers. In this paper, we propose a coverless video steganography algorithm based on semantic segmentation. Specifically, to establish the mapping relationship between secret information and video files effectively, this paper introduces the deep learning based on semantic segmentation network to calculate the statistical histogram of semantic information. To quickly index the sender's secret message to the corresponding video frame, we build a three-digit index structure. The receiver can extract the valid video frame from the three-digit index information and restore the secret information. On the one hand, the neural network is trained through the original image and the noisy image in this scheme; therefore, it can not only effectively resist the interference of noises, but also accurately extract the robust deep features of the image. The frames of video generate the robust mapping to the secret information after the semantic information statistics. On the other hand, semantic segmentation belongs to pixel-level segmentation, which has high requirements for network parameters, so it is difficult for attackers to decrypt and recover secret information. Since this scheme does not modify the primitiveness of video data, it can effectively resist steganalysis tools. The experimental results and analysis show that the video coverless information hiding scheme has a large capacity and a certain resistance to noise attack.

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Section: Semantic Segmentation Based On Convolutional Neural Networkmentioning

confidence: 99%

A video coverless information hiding algorithm based on semantic segmentation

Pan

Qin

Tan

et al. 2020

J Image Video Proc.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Due to the explosive growth of data, a large number of CNNs have been proposed in the past decade(AlexNet [21], VGGNet [22], GoogLeNet [23], ResNet [24], and DenseNet [25]). CNNs have been widely applied to steganalysis [26], image classification, and image recognition such as CAPTCHA recognition [27,28], food recognition [29], citrus diseases recognition [30], and image retrieval [31][32][33]. Therefore, most existing deep learning networks in the field of image processing either combine them or make improvements based on them.…”

Section: Preliminariesmentioning

confidence: 99%

Coverless image steganography based on DenseNet feature mapping

Liu

Xiang

Qin

et al. 2020

J Image Video Proc.

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Since the concept of coverless information hiding was proposed, it has been greatly developed due to its effectiveness of resisting the steganographic tools. Most existing coverless image steganography (CIS) methods achieve excellent robustness under non-geometric attacks. However, they do not perform well under some geometric attacks. Towards this goal, a CIS algorithm based on DenseNet feature mapping is proposed. Deep learning is introduced to extract high-dimensional CNN features which are mapped into hash sequences. For the sender, a binary tree hash index is built to accelerate index speed of searching hidden information and DenseNet hash sequence, and then, all matched images are sent. For the receiver, the secret information can be recovered successfully by calculating the DenseNet hash sequence of the cover image. During the whole steganography process, the cover images remain unchanged. Experimental results and analysis show that the proposed scheme has better robust compared with the state-of-the-art methods under geometric attacks.

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“…In recent years, the application of deep neural networks has spread across many fields, its powerful feature extraction and feature representation capabilities have enabled it to achieve impressive results in various fields. For example, in the field of verification code recognition, Wang et al [9] used the DenseNet model and adopted cross-layer connections to improve the recognition accuracy while reducing the problem of gradient disappearance and reducing the number of parameters; Chen et al [10] based on the deep learning method, through the intermediate layer of the pretrained deep learning model to output the convolution results, combined with the positive mean vector method to establish a visual feature vector database, to achieve automatic image annotation. At the same time, image hiding based on deep neural networks has also appeared in recent years.…”

Section: Introductionmentioning

confidence: 99%

StegoPNet: Image Steganography With Generalization Ability Based on Pyramid Pooling Module

et al. 2020

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In terms of payload capacity and visual effects, the existing image steganography technology based on deep neural networks still needs improvement, to solve this problem, this paper proposes a new deep convolutional steganography network based on the pyramid pooling module to achieve better image steganography. The deep convolutional neural network itself can extract features efficiently. Based on the combination of up-sampling structure, we added a pyramid pool module, under the premise of ensuring safety, fully integrated the previous important global features, achieved good hiding and extraction effects, fully integrated the previous important global features, and effective it reduces the loss of contextual information between different sub-regions in the feature extraction process and achieves better hiding and extraction effects under the premise of ensuring security. Experiments show that the average peak signalto-noise ratio (PSNR)/structure similarity (SSIM) and other indicators between the images obtained by this method have achieved good results in the experiment. Also, we have verified through ablation experiments that the pyramid pooling module can enhance the steganography effect of the network model and can further cut down the loss function of the model. INDEX TERMS Image steganography, deep neural network, pyramid pooling module.

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CAPTCHA recognition based on deep convolutional neural network

Cited by 76 publications

References 17 publications

A video coverless information hiding algorithm based on semantic segmentation

A video coverless information hiding algorithm based on semantic segmentation

Coverless image steganography based on DenseNet feature mapping

StegoPNet: Image Steganography With Generalization Ability Based on Pyramid Pooling Module

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