Deep Learning Based Web Data Classification Techniques for Forensic Analysis: An Overview
Keywords:
DeepFake Detection, Deep Learning, Forensic, Web DataAbstract
The rapid advancement of artificial intelligence (AI), machine learning (ML), and deep learning (DL) over the course of the previous several decades has led to the development of a variety of novel methodologies and tools for the manipulation of audiovisual. Even though technological advancement has been utilized for the most part in acceptable uses, like being used for entertaining and educational purposes, etc., fraudulent people have nonetheless found ways to abuse it for illegal or evil objectives. For instance, high-quality and convincingly authentic fake videos, photos, or audio recordings have been produced with the goals of disseminating false data and disinformation, sowing the seeds of political dissension and hatred, and even intimidating and blackmailing individuals. Deepfake is a relatively new term that refers to videos that have been modified but nevertheless maintain a high level of quality and realism. This application's intuitive qualities have contributed to a widespread rise in its appeal among the general public, and it is currently being utilized in a variety of fields, including fraudulent transactions, online criminal activity, politics, and possibly military operations. Therefore, it is of the utmost need to establish a variety of methods for detection that are capable of doing away with this kind of forgery and putting up an entirely novel approach in audio as well as video forensics. In this research work, the numerous detection strategies are presented that have been currently under investigation in the field of Deepfake research. So as to serve as the backbone for the creation of a new technique that would be more compressible and effective in identifying the presence of Deepfakes, this will be necessary. Also, a research investigation was conducted to compare different methods that are used in conventional methods with those that are used in state-of-the-art approaches. The investigation came to the conclusion that the majority of the methodologies that are used in conventional approaches are processes that take a lot of time, require skill and understanding of the technology for the individual attempting to use them, and so on. An introduction is given to the technological issues posed by DeepFake detection, as well as the methods researchers use to devise potential solutions to this issue. The benefits and disadvantages for every sort of solution, in addition to any possible hazards and disadvantages, are dissected and analyzed here. Despite this advancement, there are still a variety of significant issues that need to be fixed before present DeepFake detection approaches can be considered fully viable. Several of these issues are brought to light, and a discussion of the investigation prospects available in this field follows.
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