Ozcinar, CagriAnbarjafari, GholamrezaKarabulut, DogusTartu Ülikool. Loodus- ja täppisteaduste valdkondTartu Ülikool. Tehnoloogiainstituut2021-05-312021-05-312020http://hdl.handle.net/10062/72115Millions of users produce and consume billions of content on social media. Therefore, humanreviewed content moderation is not achievable in such volume. Automating content moderation is a scalable solution for social media platforms. In this thesis work, we propose a neural networks based automatic content moderation pipeline. Our solution consists of two main parts: the first part that classifies the content into granular content classes and a second part that automatically obfuscates the part of the image that might be inappropriate for the target audience. The proposed solution is cost-efficient in terms of human labour. Our classification network is trained with automatically labelled data using noise-robust techniques. Our automatic obfuscation algorithm uses the information obtained from the classification network and does not require additional annotation or supplementary training. This obfuscation algorithm presents a novel-use case to the state-of-the-art.engopenAccessAttribution-NonCommercial-NoDerivatives 4.0 InternationalComputer visionImage recognitionPattern recognitionConvolutional neural networksDeep learningAutomated content moderationarvutinägeminepildituvastusmustrituvastuskonvolutsioonilised närvivõrgudsügav õppimineautomatiseeritud sisu modereeriminemagistritöödinfo:eu-repo/semantics/masterThesis