International Journal of Research and Development in Engineering Sciences https://www.ijrdes.com D. Ramachandra Reddy 6 2 (March - April) 2024 04 March-April enFake DetectionDeep LearningText AnalysisDeep fake imagesFeature Extraction. In recent years the proliferation of deepfake technology has raised significant concerns regarding the authenticity and trustworthiness of digital media Deepfakes which are highly realistic manipulations of audio and visual content created using artificial intelligence techniques pose a serious threat to various aspects of society including misinformation privacy infringement and cybersecurity breaches Detecting and mitigating the spread of deepfakes has become a critical priority for researchers and practitioners in the field of computer vision and artificial intelligence In this context the development of advanced deep learning models for deepfake detection has emerged as a promising approach to address this pressing challengeThis research develops an advanced deep learning model to identify modified material in order to combat the rising issue of fraudulent videos The process entails importing datasets removing frames from films locating landmarks on the face and examining the frames to find abnormalities typical of deep fakes There are several CNN architectures used including ResNet50 and EfficientNetB2 CNN and sequential architectures eg InceptionV3 GRU EfficientNetB2 GRU are effective The litmus test for the efficacy of our model lies in its evaluation using the Deep Fake Detection Challenge DFDC datasetan industry benchmark recognized for gauging the prowess of deepfake detection systems In addition to model refinement we proffer a proactive strategy to counteract the pervasive dissemination of deepfakes Copyright (c) 2026 International Journal of Research and Development in Engineering Sciences. All rights reserved. 2024 All Rights Reserved