Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

2019

DOI: 10.1007/978-3-030-13469-3_64

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Spatiotemporal CNNs for Pornography Detection in Videos

Murilo Varges da Silva

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Aparecido Nilceu Marana

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Introduction2

Related Work1

Types Of Strategies To Detect Sexually Sensitive Content Cla...1

Pornography Detection1

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Cited by 15 publications

(6 citation statements)

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“…da Silva and Marana (2019) asserted that inappropriate content represents a significant concern regarding Internet access for young people and children. The researchers conducted an investigation using the Pornography‐800 data set, employing two spatial–temporal CNN models, namely VGG‐C3D CNN and R(2 + 1)D models, to detect pornography in videos.…”

Section: Related Workmentioning

confidence: 99%

DeepCens: A deep learning‐based system for real‐time image and video censorship

Yuksel,

Tan

2023

Expert Systems

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The popularity of social networks and video‐on‐demand platforms has increased the importance of image and video censorship. These platforms contain various content such as violence, explicit content, drug use and smoking that may be offensive or harmful to certain viewers. As a result, censorship is employed to filter or remove content that is unsuitable for particular audiences such as children and teenagers. However, policies for censoring harmful content in digital environments are either limited or nonexistent. This underscores the need for automated systems that can detect and censor harmful content in real time. To address these challenges, we developed the first of our knowledge systems using deep learning techniques to censor harmful content. We propose two novel, YOLO‐based real‐time censorship algorithms. Our approaches employ a pipeline‐based architecture that parallelizes the operations with subprocesses. In our experiments, the proposed algorithms performed faster and with higher accuracies compared to traditional approaches. Specifically, its content‐based accuracies were 98% for explicit content, 97% for alcohol, 98% for cigarettes and 97% for violence. Our research highlights the importance of developing effective and efficient solutions for censoring harmful content on digital media platforms. Our deep learning‐based system represents a promising approach to this challenge and has the potential to enhance user safety and protect vulnerable groups from harmful and offensive content. Future research will continue to refine and improve such systems to better address the evolving landscape of digital media and the challenges posed by harmful content.

“…da Silva and Marana (2019) asserted that inappropriate content represents a significant concern regarding Internet access for young people and children. The researchers conducted an investigation using the Pornography‐800 data set, employing two spatial–temporal CNN models, namely VGG‐C3D CNN and R(2 + 1)D models, to detect pornography in videos.…”

Section: Related Workmentioning

confidence: 99%

DeepCens: A deep learning‐based system for real‐time image and video censorship

Yuksel,

Tan

2023

Expert Systems

View full text Add to dashboard Cite

The popularity of social networks and video‐on‐demand platforms has increased the importance of image and video censorship. These platforms contain various content such as violence, explicit content, drug use and smoking that may be offensive or harmful to certain viewers. As a result, censorship is employed to filter or remove content that is unsuitable for particular audiences such as children and teenagers. However, policies for censoring harmful content in digital environments are either limited or nonexistent. This underscores the need for automated systems that can detect and censor harmful content in real time. To address these challenges, we developed the first of our knowledge systems using deep learning techniques to censor harmful content. We propose two novel, YOLO‐based real‐time censorship algorithms. Our approaches employ a pipeline‐based architecture that parallelizes the operations with subprocesses. In our experiments, the proposed algorithms performed faster and with higher accuracies compared to traditional approaches. Specifically, its content‐based accuracies were 98% for explicit content, 97% for alcohol, 98% for cigarettes and 97% for violence. Our research highlights the importance of developing effective and efficient solutions for censoring harmful content on digital media platforms. Our deep learning‐based system represents a promising approach to this challenge and has the potential to enhance user safety and protect vulnerable groups from harmful and offensive content. Future research will continue to refine and improve such systems to better address the evolving landscape of digital media and the challenges posed by harmful content.

“…Therefore, this methodology has been used to detect pornography in images and videos based on recent successful DL solutions. Thus, the convolutional neural network (CNN) and the recurrent neural network (RNN) architectures (or combinations of the above) have been proposed in this domain [28]. In addition, in recent years, some classical CNN architectures such as AlexNet, VGG, ResNets and other RNN and long short-term memory (LSTM) have been extended by researchers [29][30][31][32][33], and more advanced ones, such as transformers, initially applied in natural language processing (NLP), have shown strong interest [33][34][35].…”

Section: Types Of Strategies To Detect Sexually Sensitive Content Cla...mentioning

confidence: 99%

Learning Strategies for Sensitive Content Detection

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et al. 2023

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Currently, the volume of sensitive content on the Internet, such as pornography and child pornography, and the amount of time that people spend online (especially children) have led to an increase in the distribution of such content (e.g., images of children being sexually abused, real-time videos of such abuse, grooming activities, etc.). It is therefore essential to have effective IT tools that automate the detection and blocking of this type of material, as manual filtering of huge volumes of data is practically impossible. The goal of this study is to carry out a comprehensive review of different learning strategies for the detection of sensitive content available in the literature, from the most conventional techniques to the most cutting-edge deep learning algorithms, highlighting the strengths and weaknesses of each, as well as the datasets used. The performance and scalability of the different strategies proposed in this work depend on the heterogeneity of the dataset, the feature extraction techniques (hashes, visual, audio, etc.) and the learning algorithms. Finally, new lines of research in sensitive-content detection are presented.

“…da Silva and Marana [51] experimented with spatiotemporal CNN networks using VGG-C3D CNN and ResNet R(2 + 1)D CNN. They compared performance to other CNN based methods for pornography detection, without combining them with other motion features such as optic flow or IDT.…”

Section: Pornography Detectionmentioning

confidence: 99%

A Survey of Deep Learning Solutions for Multimedia Visual Content Analysis

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et al. 2019

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The increasing use of social media networks on handheld devices, especially smartphones with powerful built-in cameras, and the widespread availability of fast and high bandwidth broadband connections, added to the popularity of cloud storage, is enabling the generation and distribution of massive volumes of digital media, including images and videos. Such media is full of visual information and holds immense value in today's world. The volume of data involved calls for automated visual content analysis systems able to meet the demands of practice in terms of efficiency and effectiveness. Deep learning (DL) has recently emerged as a prominent technique for visual content analysis. It is data-driven in nature and provides automatic end-to-end learning solutions without the need to rely explicitly on predefined handcrafted feature extractors. Another appealing characteristic of DL solutions is the performance they can achieve, once the network is trained, under practical constraints. This paper identifies eight problem domains which require analysis of visual artifacts in multimedia. It surveys the recent, authoritative, and the best performing DL solutions and lists the datasets used in the development of these deep methods for the identified types of visual analysis problems. This paper also discusses the challenges that the DL solutions face which can compromise their reliability, robustness, and accuracy for visual content analysis. INDEX TERMS Visual content analysis, deep learning, machine learning, dataset.

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