The dataset gathers a large-scale corpus of pornographic/non-pornographic images and videos containing a rich diversity of context. As we recognize, the LSPD dataset is the first ever dataset for both object detection and image/video classification tasks in this area. This paper introduces a new dataset named Large-Scale Pornographic Dataset for detection and classification (LSPD) that intends to advance the standard quality of pornographic visual content classification and sexual object detection tasks. Method outshines all of the identified state-of-the-art literature methods. On four benchmark datasets with Euclidean and city-block distances. Selects robust ones from the set of features. PCA with a 99.99% variance preserves healthy features, and LDA

The proposed work uses a variancebasedĪpproach for choosing the number of principal components/eigenvectors

The optimalįeatures are computed with the help of principal component analysis (PCA)Īnd linear discriminant analysis (LDA). Wavelet transform, resulting in powerful textural features). Improved color coherence vector (ICCV) and texture features with a gray-levelĬo-occurrence matrix (GLCM) along with DWT-MSLBP (which is derived fromĪpplying a modified multi-scale local binary pattern over a discrete This work presents a novel framework for retrieving similar imagesīased on color and texture features. A CBIR system measures the similaritiesīetween a query and the image contents in a dataset and ranks the dataset The obfuscation algorithm covers a minimum explicitly nude area of 0.68 on average.Ĭontent-based image retrieval (CBIR) retrieves visually similar images from aĭataset based on a specified query. The classification network achieves a top-1 accuracy of 0.903 and a top-2 accuracy of 0.986. This obfuscation algorithm presents a novel-use case of class-specific activation mappings for censoring regional explicit nudity in images.

Our automatic obfuscation algorithm uses the information obtained from the classification network and does not require additional annotation or supplementary training. Our classification network is trained with automatically labelled data using noise-robust techniques.

Our proposed solution is a cost-efficient in terms of human labour and practical for deploying the real-time systems. Our solution consists of two main parts: the first part classifies a given image into granular content classes and a second part obfuscates the part of a given image that might be inappropriate for the target audience. In this research work, we propose an automatic content moderation pipeline based on deep neural networks. Automating content moderation is a scalable solution for social media platforms. Therefore, human-reviewed content moderation is not achievable in such volume. Millions of users produce and consume billions of content on social media.

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