Abstract:
“Deepfakes” have seen a dramatic rise in recent times and are becoming quite realistic
and indistinguishable with the advancement of deepfake generation techniques. Promising strides
have been made in the deepfake detection area even though it is a relatively new research domain.
Majority of current deepfake detection solutions only classify a video as a deepfake without
providing any explanations behind the prediction. However, these works fail in situations where
transparency behind a tool’s decision is crucial, especially in a court of law, where digital forensic
investigators maybe called to testify if a video is a deepfake with evidence; or where justifications
behind tool decisions plays a key role in the jury’s verdict. Explainable AI (XAI) has the power
to make deepfake detection more meaningful, as it can effectively help explain why the detection
tool classified the video as a deepfake by highlighting forged super-pixels of the video frames.
This paper proposes the use of “Anchors” XAI method, a model-agnostic high precision explainer
to build the prediction explainer model, that can visually explain the predictions of a deepfake
detector model built on top of the EfficientNet architecture. Evaluation results show that Anchors
fair better than LIME in terms of producing visually explainable and easily interpretable
explanations and produces an anchor affinity score of 70.23%. The deepfake detector model
yields an accuracy of 91.92%.
