Obscene image detection using transfer learning and feature fusion.

Deep learning-based methods have been proven excellent performance in detecting pornographic images/videos flooded on social media. However, in a dearth of huge yet well-labeled datasets, these methods may suffer from under/overfitting problems and may exhibit unstable output responses in the classification process. To deal with the issue we have suggested an automatic pornographic image detection method by utilizing transfer learning (TL) and feature fusion. The novelty of our proposed work is TL based feature fusion process (FFP) which enables the removal of hyper-parameter tuning, improves model performance, and lowers the computational burden of the desired model. FFP fuses low-level and mid-level features of the outperforming pre-trained models followed by transferring the learned knowledge to control the classification process. Key contributions of our proposed method are i) generation of a well-labeled obscene image dataset GGOI via Pix-2-Pix GAN architecture for the training of deep learning models ii) modification of model architectures by integrating batch normalization and mixed pooling strategy to obtain training stability (iii) selection of outperforming models to be integrated with the FFP by performing end-to-end detection of obscene images and iv) design of TL based obscene image detection method by retraining the last layer of the fused model. Extensive experimental analyses are performed on benchmark datasets i.e., NPDI, Pornography 2k, and generated GGOI dataset. The proposed TL model with fused MobileNet V2 + DenseNet169 network performs as the state-of-the-art model compared to existing methods and provides average classification accuracy, sensitivity, and F1 score of 98.50, 98.46 and 98.49 respectively.