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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