Media Forensics and Deepfake Systematic Survey
Nadeem Jabbar CH, Aqib Saghir, Ayaz Ahmad Meer, Salman Ahmad Sahi,, Bilal Hassan, and Siddiqui Muhammad Yasir

TL;DR
This survey comprehensively reviews Deepfake creation and detection methods, analyzing datasets, techniques, challenges, and recent developments to advance understanding and future research in media forensics.
Contribution
It provides an in-depth categorization of Deepfake techniques, evaluates datasets, and discusses recent progress and obstacles in detecting fake facial images.
Findings
Deepfake datasets have evolved significantly over time.
A general Deepfake detection model using deep learning is proposed.
Challenges remain in reliably detecting sophisticated Deepfakes.
Abstract
Deepfake is a generative deep learning algorithm that creates or changes facial features in a very realistic way making it hard to differentiate the real from the fake features It can be used to make movies look better as well as to spread false information by imitating famous people In this paper many different ways to make a Deepfake are explained analyzed and separated categorically Using Deepfake datasets models are trained and tested for reliability through experiments Deepfakes are a type of facial manipulation that allow people to change their entire faces identities attributes and expressions The trends in the available Deepfake datasets are also discussed with a focus on how they have changed Using Deep learning a general Deepfake detection model is made Moreover the problems in making and detecting Deepfakes are also mentioned As a result of this survey it is expected that the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings · Focus
