A Pornographic Images Recognition Model based on Deep One-Class Classification With Visual Attention Mechanism

The existing approaches usually treat the recognition of pornographic images as a binary or multi-class classification task, which is realized by extracting a variety of features. However, these approaches ignore the problem of infinite kinds in the negative samples and lose sight of the impact of uncertainty in classification tasks, resulting in inadequate negative data sets and incorrect recognition of samples that are not in the training set. In order to address this challenge, this paper proposes a method named Deep One-Class with Attention for Pornography (DOCAPorn) that recognizes the pornographic images through the one-class classification model based on neural networks and introduces the visual attention mechanism to enhance the performance of recognition. Moreover, since the existing approaches based on deep convolutional neural networks (CNNs) require a fixed-size (e.g., $224\times224$ ) input image, the geometric distortion caused by image scaling is not considerd in the existing approaches, which reduces the pornographic image recognizition accuracy. In order to solve this issue, this paper proposes the Scale Constraint Pooling (SCP) that converts the inputs of different dimensions into outputs of the same dimension. In addition, all the existing approaches ignore the adversarial attacks in the field of pornographic image recognition. In order to deal with this problem, the paper proposes the Preprocessing for Compressing and Reconstructing (PreCR), a pre-processing approach that reduces the subtle perturbation through compressing the images and then reconstructs the purified image for recognition. The proposed approach is verified by conducting comparative experiments using custom datasets. The experimental results showed that we achieved an accuracy of 98.419% on our dataset. In addition, the proposed approach yielded a recognition accuracy of 95.632% on the NPDI dataset. Furthermore, the obtained results demonstrate that the proposed approach not only effectively recognizes pornographic images but also effectively defends the adversarial attacks.