Explicit Content Detection System: An Approach towards a Safe and Ethical Environment

Bhatti, Ali Qamar; Umer, Muhammad Fahad; Adil, Syed Hasan; Ebrahim, Mansoor; Nawaz, Daniyal; Ahmed, Faizan

doi:10.1155/2018/1463546

Cited by 13 publications

(8 citation statements)

References 6 publications

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“…The original documentation for ResNet50 [13] did not provide a detailed architectural diagram either. Consequently, we are utilizing a diagram from a study conducted by Hindawi [14] to illustrate the model structure.…”

Section: Description Of Proposed Solution/designmentioning

confidence: 99%

Vehicle Type Classification with Small Dataset and Transfer Learning Techniques

Pham,

Can

et al. 2024

EAI Endorsed Trans Ind Net Intel Syst

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This study delves into the application of deep learning training techniques using a restricted dataset, encompassing around 400 vehicle images sourced from Kaggle. Faced with the challenges of limited data, the impracticality of training models from scratch becomes apparent, advocating instead for the utilization of pre-trained models with pre-trained weights. The investigation considers three prominent models—EfficientNetB0, ResNetB0, and MobileNetV2—with EfficientNetB0 emerging as the most proficient choice. Employing the gradually unfreeze layer technique over a specified number of epochs, EfficientNetB0 exhibits remarkable accuracy, reaching 99.5% on the training dataset and 97% on the validation dataset. In contrast, training models from scratch results in notably lower accuracy. In this context, knowledge distillation proves pivotal, overcoming this limitation and significantly improving accuracy from 29.5% in training and 20.5% in validation to 54% and 45%, respectively. This study uniquely contributes by exploring transfer learning with gradually unfreeze layers and elucidates the potential of knowledge distillation. It highlights their effectiveness in robustly enhancing model performance under data scarcity, thus addressing challenges associated with training deep learning models on limited datasets. The findings underscore the practical significance of these techniques in achieving superior results when confronted with data constraints in real-world scenarios

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Section: Description Of Proposed Solution/designmentioning

confidence: 99%

Vehicle Type Classification with Small Dataset and Transfer Learning Techniques

Pham,

Can

et al. 2024

EAI Endorsed Trans Ind Net Intel Syst

View full text Add to dashboard Cite

show abstract

“…Using optical flow and MPEG motion vectors an unique method was proposed for fusing static and dynamic motion data for obscene video classification which yielded a classification accuracy of 97.9% with an error reduction of 64.4% (Perez et al, 2017). In method (Qamar Bhatti et al, 2018), pornography was classified using a ResNet‐50‐based residual network, which achieved an accuracy of 95%. ACRODE, a combination of LSTM and CNN, was developed to categorize pornographic content using the NPDI dataset, and it achieved 95.3% accuracy (Wehrmann et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

“…Skin colour segmentation, bag of words (BoWs) approach, visual and motion feature techniques (Adnan & Nawaz, 2016; Caetano et al, 2016; Tripathi & Piccinelli, 2008; Yin et al, 2012) and deep learning algorithms (Agastya et al, 2018; Cheng et al, 2019; Nurhadiyatna et al, 2017; Perez et al, 2017; Qamar Bhatti et al, 2018; Wehrmann et al, 2018) are a handful of good techniques developed for recognizing pornographic material. Initially, obscene image or video classification was centred on human skin volume with the notion that higher volumes of detected skin would indicate a greater likelihood of nudity.…”

Section: Introductionmentioning

confidence: 99%

SBMYv3: Improved MobYOLOv3 a BAM attention‐based approach for obscene image and video detection

et al. 2023

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Countless cybercrime instances have shown the need for detecting and blocking obscene material from social media sites. Deep learning methods (DLMs) outperformed in recognizing obscene content flooded on many online platforms. However, these contemporary DLMs primarily treat the recognition of obscene content as a simple task of binary classification, rather than focusing on the labelling of obscene areas. Hence, many of these methods could not pay attention to the fact that misclassification samples are so diverse. Therefore, this paper focuses on two aspects (i) developing a deep learning model that could classify and label the obscene portion, and (ii) generating a labelled obscene image dataset with a wide variety of obscene samples to minimize the risks of inaccurate recognition. We have proposed a method named S3Pooling based bottleneck attention module (BAM) embedded MobileNetV2‐YOLOv3 (SBMYv3) for automatic detection of obscene content using an attention mechanism and a suitable pooling strategy. The key contributions of our article are: (i) generation of a well‐labelled obscene image dataset with a variety of augmentation strategies using Pix‐2‐Pix GAN (ii) modifications to the backend architecture of YOLOv3 using MobileNetV2 and BAM to ensure focused and accurate feature extraction, and (iii) selection of an optimal pooling strategy, that is, S3Pooling strategy, while taking the design of the feature extractor into account. The proposed SBMYv3 model outperformed other state‐of‐the‐art models with 99.26% testing accuracy, 99.39% recall, 99.13% precision, and 99.13% IoU values respectively.

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“…With an accuracy of 75.08%, ResNet‐34 was found to be the most effective model. In (Qamar Bhatti et al, 2018), trained a Resnet‐50 model for obscene classification with 1000 obscene and 1000 non‐obscene photos. The model achieved a respectable 95% accuracy.…”

Section: Related Workmentioning

confidence: 99%

ASYv3: Attention‐enabled pooling embedded Swin transformer‐based YOLOv3 for obscenity detection

et al. 2023

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The rampant spread of explicit content across social media can leave a damaging mark on our society. Hence, the need to be vigilant in detecting and curtailing sexually explicit content cannot be overstated. As such, it becomes paramount to discern and manage sexually explicit material to curb its dissemination and safeguard our digital communities from its harmful effects. In this article, we propose a unique technique entitled attention‐enabled pooling (ABP) embedded Swin transformer‐based YOLOv3 (ASYv3) for the detection of obscene areas present in the images with a bounding box around the offensive regions. ASYv3 employs a unique two‐step approach for enhanced performance in obscene detection. In the first step, a scalable and efficient Swin transformer block is integrated, utilizing self‐attention and model parallelism to train massive models effectively. In the second phase, the embedding layer of the Swin transformer is replaced with ABP, mitigating disruption of feature context. ABP allows for the projection of raw‐valued features into linear form with proper attention to feature context information at specified locations, resulting in optimized feature extraction. The proposed ABP embedded Swin transformer‐based YOLOv3 (ASYv3) was trained with annotated obscene images (AOI) dataset. The proposed ASYv3 model surpassed the state‐of‐the‐art methods by achieving 97% testing accuracy, 96.62% precision, 97.40% sensitivity, 3.48% FPR rate, 97.37% NPV values, and 95.59% mAP values, respectively.

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Explicit Content Detection System: An Approach towards a Safe and Ethical Environment

Cited by 13 publications

References 6 publications

Vehicle Type Classification with Small Dataset and Transfer Learning Techniques

Vehicle Type Classification with Small Dataset and Transfer Learning Techniques

SBMYv3: Improved MobYOLOv3 a BAM attention‐based approach for obscene image and video detection

ASYv3: Attention‐enabled pooling embedded Swin transformer‐based YOLOv3 for obscenity detection

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