Sensitive Image Classification by Vision Transformers

TL;DR

This paper demonstrates that vision transformer models outperform traditional CNNs and other methods in classifying sensitive images, such as pornographic content, by effectively capturing global interactions and reducing ambiguity.

Contribution

The study introduces the application of vision transformers to sensitive image classification, showing their superiority over conventional models and established methods in this domain.

Findings

Vision transformers outperform ResNet models in classification accuracy.

They effectively reduce ambiguity in attention maps for sensitive images.

Transformers surpass existing CNN-based methods like Bumble and attention-based CNNs.

Abstract

When it comes to classifying child sexual abuse images, managing similar inter-class correlations and diverse intra-class correlations poses a significant challenge. Vision transformer models, unlike conventional deep convolutional network models, leverage a self-attention mechanism to capture global interactions among contextual local elements. This allows them to navigate through image patches effectively, avoiding incorrect correlations and reducing ambiguity in attention maps, thus proving their efficacy in computer vision tasks. Rather than directly analyzing child sexual abuse data, we constructed two datasets: one comprising clean and pornographic images and another with three classes, which additionally include images indicative of pornography, sourced from Reddit and Google Open Images data. In our experiments, we also employ an adult content image benchmark dataset. These datasets served as a basis for assessing the performance of vision transformer models in pornographic image classification. In our study, we conducted a comparative analysis between various popular vision transformer models and traditional pre-trained ResNet models. Furthermore, we compared them with established methods for sensitive image detection such as attention and metric learning based CNN and Bumble. The findings demonstrated that vision transformer networks surpassed the benchmark pre-trained models, showcasing their superior classification and detection capabilities in this task.