Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/158878
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dc.contributor.authorLu, Yeen_US
dc.date.accessioned2022-05-31T05:53:38Z-
dc.date.available2022-05-31T05:53:38Z-
dc.date.issued2022-
dc.identifier.citationLu, Y. (2022). Deep learning-based image forgery detection. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158878en_US
dc.identifier.urihttps://hdl.handle.net/10356/158878-
dc.description.abstractIn recent years, different kinds of image synthesis models have been developed, which are widely used in the field of deep learning and daily life. However, many people are troubled by these techniques because they can be utilized by malicious people to produce fake images. Nevertheless, different image synthesis techniques emerge one after another, and few existing image discrimination models can detect these different types of images. This dissertation discusses a model that can detect different image synthesis techniques, which has good practical application and research value. In addition, we introduce a brand new dataset, TMCface. We use this dataset and other public datasets to compare the performance of baselines with ours. Keywords: Image forensics, Generalization, DeepLearning, ResNet50, TMCface.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleDeep learning-based image forgery detectionen_US
dc.typeThesis-Master by Courseworken_US
dc.contributor.supervisorAlex Chichung Koten_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Signal Processing)en_US
dc.contributor.supervisoremailEACKOT@ntu.edu.sgen_US
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