Automated Nudity Recognition using Very Deep Residual Learning Network

Banaeeyan, Rasoul; Karim, Hezerul Abdul; Abdullah, Muhammad; Fauzi, Mohammad Faizal Ahmad; Mansor, Sarina; See, John

doi:10.35940/ijrte.c1024.1083s19

Cited by 2 publications

(1 citation statement)

References 20 publications

(38 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is adaptable to various applications and able to learn high abstract features automatically and directly from raw pixels [6]. Deep models, which learn features in an endto-end scheme, have demonstrated the proficiency in computer vision tasks and specifically in the application of nude/normal classification [7][8][9][10][11][12][13]. Convolutional neural networks (CNNs), which are well known deep neural networks, have several layers including convolutional, pooling, normalisation, activation, and fully connected.…”

Section: Introductionmentioning

confidence: 99%

Transfer Detection of YOLO to Focus CNN’s Attention on Nude Regions for Adult Content Detection

et al. 2020

View full text Add to dashboard Cite

Video pornography and nudity detection aim to detect and classify people in videos into nude or normal for censorship purposes. Recent literature has demonstrated pornography detection utilising the convolutional neural network (CNN) to extract features directly from the whole frames and support vector machine (SVM) to classify the extracted features into two categories. However, existing methods were not able to detect the small-scale content of pornography and nudity in frames with diverse backgrounds. This limitation has led to a high false-negative rate (FNR) and misclassification of nude frames as normal ones. In order to address this matter, this paper explores the limitation of the existing convolutional-only approaches focusing the visual attention of CNN on the expected nude regions inside the frames to reduce the FNR. The You Only Look Once (YOLO) object detector was transferred to the pornography and nudity detection application to detect persons as regions of interest (ROIs), which were applied to CNN and SVM for nude/normal classification. Several experiments were conducted to compare the performance of various CNNs and classifiers using our proposed dataset. It was found that ResNet101 with random forest outperformed other models concerning the F1-score of 90.03% and accuracy of 87.75%. Furthermore, an ablation study was performed to demonstrate the impact of adding the YOLO before the CNN. YOLO–CNN was shown to outperform CNN-only in terms of accuracy, which was increased from 85.5% to 89.5%. Additionally, a new benchmark dataset with challenging content, including various human sizes and backgrounds, was proposed.

show abstract

Section: Introductionmentioning

confidence: 99%

Transfer Detection of YOLO to Focus CNN’s Attention on Nude Regions for Adult Content Detection

et al. 2020

View full text Add to dashboard Cite

show abstract

Classification of Static Poses Based on Key Point Detection for Application of Incriminated Image Files

Antonia

2024

Informatik Aktuell

View full text Add to dashboard Cite

ZusammenfassungThe role of artificial intelligence, particularly in enhancing decision-making processes and facilitating automation, has become indispensable in today’s society. Law enforcement agencies have used the findings of this research for several years to analyze the ever-growing volumes of data. This approach owns an important role in the detection of child and adolescent pornography. Research conducted in this field has traditionally been dependent on the findings from skin filtering studies. However, some scientific publications and empirical studies reveal insufficient classification when using nudity levels. This work takes a more promising approach by leveraging the knowledge of an existing system and combining it with a homegrown model to improve the detection of incriminated image files. For this implementation, techniques from motion analysis were used to open up a new field of pose recognition.Zusammenfassung. Die Rolle der künstlichen Intelligenz in Bezug auf ihre Individualität und Automatisierung ist in der heutigen Gesellschaft unverzichtbar geworden. Die Ergebnisse dieser Forschung werden seit einigen Jahren ebenfalls genutzt, um Strafverfolgungsbehörden bei der Analyse der ständig wachsenden Datenmengen zu unterstützen. Dieser Ansatz besitzt eine wichtige Rolle bei der Aufdeckung von Kinder- und Jugendpornografie. Bisherige Studien in diesem Bereich stützen sich größtenteils auf die Ergebnisse von Studien zur Hautfilterung. Einige wissenschaftliche Veröffentlichungen und empirische Studien zeigen jedoch, dass die Verwendung des Nacktheitsgrades keine ausreichende Klassifizierung ermög-licht. Diese Arbeit verfolgt einen vielversprechenderen Ansatz, indem sie das Wissen eines bestehenden Systems nutzt und es mit einem selbst entwickelten Modell kombiniert, um die Erkennung inkriminierter Bilddateien zu verbessern. Für diese Implementierung wurden Techniken aus der Bewegungsanalyse verwendet, um ein neues Feld der Posenerkennung zu erschließen.

show abstract

Automated Nudity Recognition using Very Deep Residual Learning Network

Cited by 2 publications

References 20 publications

Transfer Detection of YOLO to Focus CNN’s Attention on Nude Regions for Adult Content Detection

Transfer Detection of YOLO to Focus CNN’s Attention on Nude Regions for Adult Content Detection

Classification of Static Poses Based on Key Point Detection for Application of Incriminated Image Files

Contact Info

Product

Resources

About