Abstract: Methods, apparatus, systems and articles of manufacture to improve deepfake detection with explainability are disclosed. An example apparatus includes interface circuitry to receive a media file, machine readable instructions, and at least one processor circuit to be programmed by the instructions to generate, based on a deepfake classification model, a classification score for the media file, obtain a class output from a final convolution feature map of the deepfake classification model, generate an explainability map based on a pooled weighted feature map along a channel dimension of the feature map, and identify the media file as a real or a deepfake media file based on the classification score and the explainability map.

Abstract: Methods, apparatus, systems and articles of manufacture to improve deepfake detection with explainability are disclosed. An example apparatus includes a deepfake classification model trainer to train a classification model based on a first portion of a dataset of media with known classification information, the classification model to output a classification for input media from a second portion of the dataset of media with known classification information; an explainability map generator to generate an explainability map based on the output of the classification model; a classification analyzer to compare the classification of the input media from the classification model with a known classification of the input media to determine if a misclassification occurred; and a model modifier to, when the misclassification occurred, modify the classification model based on the explainability map.

Abstract: Methods, apparatus, systems and articles of manufacture to improve deepfake detection with explainability are disclosed. An example apparatus includes a deepfake classification model trainer to train a classification model based on a first portion of a dataset of media with known classification information, the classification model to output a classification for input media from a second portion of the dataset of media with known classification information; an explainability map generator to generate an explainability map based on the output of the classification model; a classification analyzer to compare the classification of the input media from the classification model with a known classification of the input media to determine if a misclassification occurred; and a model modifier to, when the misclassification occurred, modify the classification model based on the explainability map.