Deepfake detection using the multi-attention technique was considered the fine-grained classification algorithm (Maheswaran et al., 2020). Here the gradients not considered in the image. Local features were collected efficiently, but they did not calculate all frames and gradients. This problem motivated the researcher to choose a local binary pattern with histogram computation. The research carried out in (Wang et al., 2020) had implemented the image manipulation method. It was identified by facial expression and also localized the forgery in the image. However manipulation works like photoshop and accuracy is result is not acquired. The main disadvantage of the technique was that it did not produce an optimal outcome in all images. The article had used light-weight extraction for the fake face detection in the input dataset. Further, the subspace learning technique was utilized to analyze the face image to filter the significant feature. Unfortunately, these techniques do not support dynamic models. In (Li et al., 2020), the problem of overfitting in fake face detection was tackled using deep learning models. On other side it consumes more time by concentrating on overfitting.

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