Image Restoration through InceptionResNetV2: Unveiling the True Identity within Deepfake Detected Sequences using Subsequent Deepfake Modifier

Deepfakes are explicit films created by swapping faces using neural networks. This type of artificial intelligence has been applied to complicated problems such as vote-based systems and national security. Deepfake calculations make exceedingly practical fake photographs and motion pictures that are nearly unclear from genuine ones. Subsequent Deepfake Modifier, an image-to-sequence model for detecting Subseq-DeepFake manipulation, and InceptionResNetV2 in CNN are the two deepfake detection techniques that are examined in this paper. The results offer guidance for further deepfake detection research.