2018

DOI: 10.1016/j.neucom.2018.08.080

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EFUI: An ensemble framework using uncertain inference for pornographic image recognition

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Cited by 18 publications

(16 citation statements)

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“…Another way to increase the accuracy of classification is to combine multiple classifiers. Shen et al [31] combined the results of several CNNs using Bayesian networks. Cheng et al [34] combined the features extracted by the two CNNs and adopted the final feature vector for classification.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

“…not accurate on intricate images adult image classification using CNN [27] accurate requires a large number of training images adult image classification using CNN plus data augmentation [30], or finetuning CNN [32] accurate. trainable by small training set time consuming adult image classification using combination of CNNs [31,34] very accurate very time consuming Table 1. Adult image classification methods comparison…”

Section: Modelmentioning

confidence: 99%

See 1 more Smart Citation

Time-Sensitive Adaptive Model for Adult Image Classification

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2020

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Images play an important role in modern internet communications, but not all of the images shared by the users are appropriate, and it is necessary to check and reject the inappropriate ones. Deep neural networks do this task perfectly, but it may not be necessary to use maximum power for all images. Many easier-to-identify images may be classified at a lower cost than running the full model. Also, the pressure on the system varies from time to time, so an algorithm that can produce the best possible results for different budgets is very useful. For this purpose, a deep convolutional neural network with the ability to generate several outputs from its various layers has been designed. Each output can be considered as a classifier with its own cost and accuracy. A selector is then used to select and combine the results of these outputs to produce the best possible result in the specified time budget. The selector uses a reinforcement learning model, which, despite the timeconsuming learning phase, is fast at execution time. Our experiments on challenging social media images dataset show that the proposed model can reduce the processing time by 32 % by sacrificing only 1.4 % of accuracy compared to the VGG-f network. Also, using different metrics such as F1-score and AUC (the Area Under the Curve in the accuracy vs. time budget chart), the superiority of the proposed model at different time budgets over the base model is shown.

“…Another way to increase the accuracy of classification is to combine multiple classifiers. Shen et al [31] combined the results of several CNNs using Bayesian networks. Cheng et al [34] combined the features extracted by the two CNNs and adopted the final feature vector for classification.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

“…not accurate on intricate images adult image classification using CNN [27] accurate requires a large number of training images adult image classification using CNN plus data augmentation [30], or finetuning CNN [32] accurate. trainable by small training set time consuming adult image classification using combination of CNNs [31,34] very accurate very time consuming Table 1. Adult image classification methods comparison…”

Section: Modelmentioning

confidence: 99%

Time-Sensitive Adaptive Model for Adult Image Classification

¹

,

²

,

³

2020

View full text Add to dashboard Cite

Images play an important role in modern internet communications, but not all of the images shared by the users are appropriate, and it is necessary to check and reject the inappropriate ones. Deep neural networks do this task perfectly, but it may not be necessary to use maximum power for all images. Many easier-to-identify images may be classified at a lower cost than running the full model. Also, the pressure on the system varies from time to time, so an algorithm that can produce the best possible results for different budgets is very useful. For this purpose, a deep convolutional neural network with the ability to generate several outputs from its various layers has been designed. Each output can be considered as a classifier with its own cost and accuracy. A selector is then used to select and combine the results of these outputs to produce the best possible result in the specified time budget. The selector uses a reinforcement learning model, which, despite the timeconsuming learning phase, is fast at execution time. Our experiments on challenging social media images dataset show that the proposed model can reduce the processing time by 32 % by sacrificing only 1.4 % of accuracy compared to the VGG-f network. Also, using different metrics such as F1-score and AUC (the Area Under the Curve in the accuracy vs. time budget chart), the superiority of the proposed model at different time budgets over the base model is shown.

“…The field of pornography content recognition has not been an exception and several attempts have been made to enhance the previous results by the aid of DL like those reported in [3], [4], [6], [10], [25].…”

Section: II Related Workmentioning

confidence: 99%

“…Another research in [10], proposed to use a probability model based on uncertain inferencing along with an ensemble of CNN-based classifiers for the task of adult content recognition in still images. Finally, authors in [3] proposed to use a pre-trained CNN model called ResNet-50 [26] in order to detect the sensitive pornography contents in images.…”

Section: II Related Workmentioning

confidence: 99%

Automated Nudity Recognition using Very Deep Residual Learning Network

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The exponentially growing number of pornographic material has brought many challenges to the modern daily life, particularly where children and minors have unlimited access to the internet. In Malaysia, all local and foreign films should obtain the suitability approval before distribution or public viewing, and this process of screening visual contents of all the TV channels imposes a huge censorship cost to the service providers such as Unifi TV. To leverage this issue, this paper proposes to use an emerging model of Deep Learning (DL) techniques called Residual Learning Convolutional Neural Networks (ResNet), in order to automate the process of nudity detection in visual contents. The pre-trained ResNet model, with hundred and one layers, was utilized to perform transfer learning and solve a new binary classification problem of nudity versus non-nudity. The performance of the proposed model is evaluated based on a newly created dataset comprising more than 4k samples of nudity and non-nudity images. After conducting experiments on the nudity dataset, the deep learning method succeeded to achieve the best performance of 70.42% in term of F-score, 84.04% in term of accuracy, and 93.72% in term of AUC .

“…Although there have been several attempts to address the problem of pornography recognition 1336 Acoustic Pornography Recognition using Fused Pitch and Mel-Frequency Cepstrum Coefficients (Caetano et al, 2016;Geng et al, 2016;Moreira et al, 2016;Nian, et al, 2016;Zhou et al, 2016;Jin et al, 2018;More et al, 2018;Nurhadiyatna et al, 2018;Shen et al, 2018;), almost all of them have utilized visual content to automate the target task of sensitive content detection.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Pornography Recognition using Fused Pitch and Mel-Frequency Cepstrum Coefficients

Lye³

et al. 2019

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The main objective of this paper is pornography recognition using audio features. Unlike most of the previous attempts, which have concentrated on the visual content of pornography images or videos, we propose to take advantage of sounds. Using sounds is particularly important in cases in which the visual features are not adequately informative of the contents (e.g., cluttered scenes, dark scenes, scenes with a covered body). To this end, our hypothesis is grounded in the assumption that scenes with pornographic content encompass audios with features specific to those scenes; these sounds can be in the form of speech or voice. More specifically, we propose to extract two types of features, (I) pitch and (II) mel-frequency cepstrum coefficients (MFCC), in order to train five different variations of the k-nearest neighbor (KNN) supervised classification models based on the fusion of these features. Later, the correctness of our hypothesis was investigated by conducting a set of evaluations based on a porno-sound dataset created based on an existing pornography video dataset. The experimental results confirm the feasibility of the proposed acoustic-driven approach by demonstrating an accuracy of 88.40%, an F-score of 85.20%, and an area under the curve (AUC) of 95% in the task of pornography recognition.

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