METHODS AND APPARATUS TO PERFORM DEEPFAKE DETECTION USING AUDIO AND VIDEO FEATURES
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.
1 . An apparatus comprising:
a first artificial intelligence-based model to output a first classification of a sound based on audio from media;
a second artificial intelligence-based model to output a second classification of the sound based on video from the media; and
a comparator to determine that the media is a deepfake based on a comparison of the first output classification to the second output classification.
2 . The apparatus of claim 1 , wherein:
the first artificial intelligence-based model is to output the first classification based on a plurality of audio frames extracted from the media within a duration of time; and
the second artificial intelligence-based model is to output the second classification based on a plurality of video frames of the media within the duration of time.
3 . The apparatus of claim 1 , further including:
an audio processing engine to generate a speech features cube based on features of the audio, the speech features cube input into the first artificial intelligence-based model to generate the first classification; and
a video processing engine to generate a video features cube based on mouth regions of humans in a video portion of the media, the video features cube input into the second artificial intelligence-based model to generate the second classification.
4 . The apparatus of claim 1 , wherein the comparator is to determine that the media is a deepfake based on a comparison of how similar the first classification is to the second classification.
5 . The apparatus of claim 1 , wherein the comparator is to determine that the media is a deepfake based on at least one of a distinction loss function or a Euclidean distance.
6 . The apparatus of claim 1 , further including a reporter to generate a report identifying the media as a deepfake.
7 . The apparatus of claim 6 , further including an interface to transmit the report to a server.
8 . The apparatus of claim 6 , wherein the reporter is to at least one of cause a user interface to generate a popup identifying that the media is a deepfake or prevent the media from being output.
9 . The apparatus of claim 1 , wherein:
the first artificial intelligence-based model includes a first convolution layer, a second convolution later, a third convolution layer, and a first fully connected layer; and
the second artificial intelligence-based model includes a fourth convolution layer, a fifth convolution later, a sixth convolution layer, and a second fully connected layer.
10 . A non-transitory computer readable storage medium comprising instructions which, when executed, cause one or more processors to at least:
output, using a first artificial intelligence-based model, a first classification of a sound based on audio from media;
output, using a second artificial intelligence-based model, a second classification of the sound based on video from the media; and
determine that the media is a deepfake based on a comparison of the first output classification to the second output classification.
11 . The computer readable storage medium of claim 10 , wherein the instructions cause the one or more processors to:
output the first classification based on a plurality of audio frames extracted from the media within a duration of time; and
output the second classification based on a plurality of video frames of the media within the duration of time.
12 . The computer readable storage medium of claim 10 , wherein the instructions cause the one or more processors to:
generate a speech features cube based on features of the audio, the speech features cube input into the first artificial intelligence-based model to generate the first classification; and
generate a video features cube based on mouth regions of humans in a video portion of the media, the video features cube input into the second artificial intelligence-based model to generate the second classification.
13 . The computer readable storage medium of claim 10 , wherein the instructions cause the one or more processors to determine that the media is a deepfake based on a comparison of how similar the first classification is to the second classification.
14 . The computer readable storage medium of claim 10 , wherein the instructions cause the one or more processors to determine that the media is a deepfake based on at least one of a distinction loss function or a Euclidean distance.
15 . The computer readable storage medium of claim 10 , wherein the instructions cause the one or more processors to generate a report identifying the media as a deepfake.
16 . The computer readable storage medium of claim 15 , wherein the instructions cause the one or more processors to cause transmission of the report to a server.
17 . The computer readable storage medium of claim 15 , wherein the instructions cause the one or more processors to at least one of cause a user interface to generate a popup identifying that the media is a deepfake or prevent the media from being output.
18 . A method comprising:
outputting, with a first artificial intelligence-based model, a first classification of a sound based on audio from media;
outputting, with a second artificial intelligence-based model, a second classification of the sound based on video from the media; and
determining, by executing an instruction with a processor, that the media is a deepfake based on a comparison of the first output classification to the second output classification.
19 . The method of claim 18 , wherein:
outputting the first classification based on a plurality of audio frames extracted from the media within a duration of time; and
outputting the second classification based on a plurality of video frames of the media within the duration of time.
20 . The method of claim 18 , further including:
generating a speech features cube based on features of the audio, the speech features cube input into the first artificial intelligence-based model to generate the first classification; and
generating a video features cube based on mouth regions of humans in a video portion of the media, the video features cube input into the second artificial intelligence-based model to generate the second classification.
21 . The method of claim 18 , further including determining that the media is a deepfake based on a comparison of how similar the first classification is to the second classification.
22 . The method of claim 18 , further including determining that the media is a deepfake based on at least one of a distinction loss function or a Euclidean distance.
23 . The method of claim 18 , further including generating a report identifying the media as a deepfake.
24 . The method of claim 23 , further including transmitting the report to a server.
25 . The method of claim 23 , further including at least one of generating a popup identifying that the media is a deepfake or preventing the media from being output.