Deepfake Detection Using AI And Machine Learning Algorithms

Deepfake technology has rapidly evolved, which makes it difficult to differentiate between real and AI-generated content. The malicious use of deep fake technology has raised some serious concerns. Deepfake face-swapping is commonly practiced across the internet and has raised a huge number of concerns in society. How to detect deepfake content is nowadays a very popular research topic. This paper presents a comprehensive study of deep fake detection techniques using Artificial intelligence (AI) and machine learning (ML) algorithms. In this work, various detection techniques have been analysed using different approaches, including audio, image, and video-based deepfake detection models. The study highlights recent advancements, challenges, methods and potential future research directions. Furthermore, the legal concerns about using deepfake technology in unethical ways have been discussed. Additionally, this paper also examines the effectiveness of different datasets and benchmarks in detecting deepfake content.

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Publication Details

Published
2025-05-15
DOI
https://doi.org/10.1109/ic2e365635.2025.11167331
Primary Topic
Currency Recognition and Detection
Type
article
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Abstract

Deepfake technology has rapidly evolved, which makes it difficult to differentiate between real and AI-generated content. The malicious use of deep fake technology has raised some serious concerns. Deepfake face-swapping is commonly practiced across the internet and has raised a huge number of concerns in society. How to detect deepfake content is nowadays a very popular research topic. This paper presents a comprehensive study of deep fake detection techniques using Artificial intelligence (AI) and machine learning (ML) algorithms. In this work, various detection techniques have been analysed using different approaches, including audio, image, and video-based deepfake detection models. The study highlights recent advancements, challenges, methods and potential future research directions. Furthermore, the legal concerns about using deepfake technology in unethical ways have been discussed. Additionally, this paper also examines the effectiveness of different datasets and benchmarks in detecting deepfake content.

Chandigarh University (IN)
Openalex Percentile: Top 74%
Currency Recognition and Detection
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