The Spiral of Digital Falsehood in Deepfakes

Leone, Massimo

doi:10.1007/s11196-023-09970-5

Cited by 6 publications

(1 citation statement)

References 51 publications

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“…Early approaches focused on detecting manipulated images and videos using handcrafted features and traditional machine learning algorithms such as Support Vector Machines (SVM) and Random Forests. However, these methods are limited in their ability to detect deepfakes that are visually indistinguishable from authentic content [5].…”

Section: Related Workmentioning

confidence: 99%

Novel Convolutional Neural Networks based Jaya algorithm Approach for Accurate Deepfake Video Detection

Hussain¹,

Ibraheem

2023

MJCS

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Deepfake videos are becoming an increasing concern due to their potential to spread misinformation and cause harm. In this paper, we propose a novel approach for accurately detecting deepfake videos using the combination of Convolutional Neural Networks (CNNs) with the Jaya algorithm optimization. The approach is evaluated on two publicly available datasets, the DeepFake Detection Challenge (DFDC) dataset and the Celeb-DF dataset, and achieves state-of-the-art performance on both datasets. Our approach achieves an accuracy of 99.3% on the DFDC dataset and 97.6% on the Celeb-DF dataset, with high F1 scores indicating a high precision and recall for detecting deepfake videos. Furthermore, our approach is more robust against adversarial attacks than existing state-of-the-art methods. The combination of CNNs with the Jaya algorithm optimization enables effective capture of the temporal information in the video sequence, while the use of robust evaluation metrics ensures objective measurement and comparison with existing methods. Our proposed approach offers a highly effective solution for detecting deepfake videos, which has the potential to be a valuable tool for media forensics, content moderation, and cyber security.

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Section: Related Workmentioning

confidence: 99%