國立虎尾科技大學 |

Leveraging Machine Learning & Deep Learning Methodologies to Detect Deepfakes.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Leveraging Machine Learning & Deep Learning Methodologies to Detect Deepfakes./
作者:	Tiwari, Aniruddha.
面頁冊數:	1 online resource (102 pages)
附註:	Source: Masters Abstracts International, Volume: 85-11.
Contained By:	Masters Abstracts International85-11.
標題:	Computer engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798382731537

Leveraging Machine Learning & Deep Learning Methodologies to Detect Deepfakes.
Tiwari, Aniruddha.

Leveraging Machine Learning & Deep Learning Methodologies to Detect Deepfakes. - 1 online resource (102 pages)

Source: Masters Abstracts International, Volume: 85-11.

Thesis (M.S.)--Minnesota State University, Mankato, 2024.

Includes bibliographical references

The rapid evolution of deep learning (DL) and machine learning (ML) techniques has facilitated the rise of highly convincing synthetic media, commonly referred to as deepfakes. These manipulative media artifacts, generated through advanced artificial intelligence algorithms, pose significant challenges in distinguishing them from authentic content. Given their potential to be disseminated widely across various online platforms, the imperative for robust detection methodologies becomes apparent. Accordingly, this study explores the efficacy of existing ML/DL-based approaches and aims to compare which type of methodology performs better in identifying deepfake content.In response to the escalating threat posed by deepfakes, previous research efforts have focused on inventing detection models leveraging CNN architectures. However, despite promising results, many of these models exhibit limitations in reproducibility and practicality when confronted with real-world scenarios. To address these challenges, this study endeavors to develop a more generalized detection framework capable of discerning deepfake content across diverse datasets. By training simple yet effective ML and DL models on a curated Wilddeepfake dataset, this research assesses the viability of detecting authentic media from deepfake counterparts. Through comparative analysis and evaluation of model performance, this study aims to contribute to the advancement of reliable deepfake detection methodologies. The models used in this study have shown significant accuracies in classifying deepfake media.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382731537Subjects--Topical Terms:

569006
Computer engineering.
Subjects--Index Terms:

Computer visionIndex Terms--Genre/Form:

554714
Electronic books.

Leveraging Machine Learning & Deep Learning Methodologies to Detect Deepfakes.
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520 $a The rapid evolution of deep learning (DL) and machine learning (ML) techniques has facilitated the rise of highly convincing synthetic media, commonly referred to as deepfakes. These manipulative media artifacts, generated through advanced artificial intelligence algorithms, pose significant challenges in distinguishing them from authentic content. Given their potential to be disseminated widely across various online platforms, the imperative for robust detection methodologies becomes apparent. Accordingly, this study explores the efficacy of existing ML/DL-based approaches and aims to compare which type of methodology performs better in identifying deepfake content.In response to the escalating threat posed by deepfakes, previous research efforts have focused on inventing detection models leveraging CNN architectures. However, despite promising results, many of these models exhibit limitations in reproducibility and practicality when confronted with real-world scenarios. To address these challenges, this study endeavors to develop a more generalized detection framework capable of discerning deepfake content across diverse datasets. By training simple yet effective ML and DL models on a curated Wilddeepfake dataset, this research assesses the viability of detecting authentic media from deepfake counterparts. Through comparative analysis and evaluation of model performance, this study aims to contribute to the advancement of reliable deepfake detection methodologies. The models used in this study have shown significant accuracies in classifying deepfake media.
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