T
Tajuddin Manhar Mohammed
Publications - 17
Citations - 478
Tajuddin Manhar Mohammed is an academic researcher. The author has contributed to research in topics: Deep learning & Malware. The author has an hindex of 8, co-authored 16 publications receiving 302 citations.
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Journal ArticleDOI
Detecting GAN generated Fake Images using Co-occurrence Matrices.
Lakshmanan Nataraj,Tajuddin Manhar Mohammed,B.S. Manjunath,Shivkumar Chandrasekaran,Arjuna Flenner,Jawadul H. Bappy,Amit K. Roy-Chowdhury +6 more
TL;DR: A novel approach to detect GAN generated fake images using a combination of co-occurrence matrices and deep learning, which achieves more than 99% classification accuracy in both datasets.
Proceedings ArticleDOI
Detection and Localization of Image Forgeries Using Resampling Features and Deep Learning
Jason Bunk,Jawadul H. Bappy,Tajuddin Manhar Mohammed,Lakshmanan Nataraj,Arjuna Flenner,B.S. Manjunath,Shivkumar Chandrasekaran,Amit K. Roy-Chowdhury,Lawrence Peterson +8 more
TL;DR: Two methods to detect and localize image manipulations based on a combination of resampling features and deep learning are proposed, both effective in detecting and localizing digital image forgeries.
Posted Content
Detection and Localization of Image Forgeries using Resampling Features and Deep Learning
Jason Bunk,Jawadul H. Bappy,Tajuddin Manhar Mohammed,Lakshmanan Nataraj,Arjuna Flenner,B.S. Manjunath,Shivkumar Chandrasekaran,Amit K. Roy-Chowdhury,Lawrence Peterson +8 more
TL;DR: In this paper, a combination of resampling features and deep learning is used to detect and localize image manipulations based on the Radon transform of features computed on overlapping image patches and a Long short-term memory (LSTM) based network for classification and localization.
Posted Content
Detecting GAN generated Fake Images using Co-occurrence Matrices
Lakshmanan Nataraj,Tajuddin Manhar Mohammed,Shivkumar Chandrasekaran,Arjuna Flenner,Jawadul H. Bappy,Amit K. Roy-Chowdhury,B.S. Manjunath +6 more
TL;DR: In this paper, a combination of co-occurrence matrices and deep learning is proposed to detect GAN generated fake images using three color channels in the pixel domain and train a model using a deep convolutional neural network (CNN) framework.
Posted Content
Detection, Attribution and Localization of GAN Generated Images
Michael Goebel,Lakshmanan Nataraj,Tejaswi Nanjundaswamy,Tajuddin Manhar Mohammed,Shivkumar Chandrasekaran,B.S. Manjunath +5 more
TL;DR: A novel approach to detect, attribute and localize GAN generated images that combines image features with deep learning methods is proposed.