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paper, we propose a Multi-scale Attention Memory with hash addressing Autoencoder network (MAMA Net) for anomaly detection. ... First, to overcome a battery of problems result from the restricted stationary receptive field of convolution operator, we coin the multi-scale global spatial attention block which can be straightforwardly ... MULTI-SCALE ATTENTION HASH MEMORY AUTOENCODER A. ...doi:10.1109/tmi.2020.3045295 fatcat:elicefjf45hunafl7oamhvx2na
This temporal-detection pipeline compares very minute visual traces on the faces of real and fake frames using Convolutional Neural Network (CNN) and stores the abnormal features for training. ... The proposed algorithm and designed network set a new benchmark for detecting the visual counterfeits and show how this system can achieve competitive results on any fake generated video or image. ... The network is loaded with the face detection model through Multi-Cascaded Convolutional Network (MTCNN) architecture. ...doi:10.1109/access.2020.2998330 fatcat:dnmk264igjfrvbfwex3awk55sa
To identify and classify DeepFakes, research in DeepFake detection using deep neural networks (DNNs) has attracted increased interest. ... Techniques for creating and manipulating multimedia information have progressed to the point where they can now ensure a high degree of realism. ... small dataset that challenges the real-world https://github.com/ dataset for DeepFake detection. deepfakeinthewild/ deepfake-in-the-wild OF  16K 173K Large-scale challenging dataset for multi-face ...doi:10.1109/access.2022.3151186 fatcat:imz6hdtofrbxfcfi6kput2mffi
S OCIAL NETWORKS are enormous sources of human-generated content. Users continuously create information, useful but hard to detect, extract, and categorize. ... Tested tasks include the extraction of emerging knowledge, represented by users similar to a given set of well-known accounts, controversy detection, obtaining controversy scores for topics discussed online ... scaled dot-product attention is the basic operation of an attention layer. ...doi:10.48676/unibo/amsdottorato/10057 fatcat:gbjtww6jabcoddn5spdpx4z4dq