A Feature Fusion Based Deep Learning Model for Deepfake Video Detection

Mohiuddin, Sk; Ganguly, Shreyan; Malakar, Samir; Kaplun, Dmitry; Sarkar, Ram

doi:10.1007/978-3-030-97020-8_18

Cited by 6 publications

(1 citation statement)

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“…We conducted a comparative analysis to investigate each of these variants and assess their relevance to the out-of-domain of deepfake detection. We note, however, that the utilization of different color spaces has already been explored in the existing deepfake detection literature, as reported in the works of [16][17][18][19] , and it will not constitute the main contributing element of our work.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Practicality and Efficiency of Deepfake Detection.

Balafrej,

Dahmane

2024

Preprint

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The proliferation of deepfake generation has become increasingly widespread. Current solutions for automatically detecting and classifying generated content require substantial computational resources, making them impractical for use by the average non-expert individual particularly from edge computing applications. In this paper, we propose a series of techniques to accelerate the inference speed of deepfake detection on video data. We also draw inspiration from steganalysis approaches to expose deepfakes as any secret payloads encoded in the image. Furthermore, some key considerations were identified to significantly reduce the size of the core convolutional neural network. The experiment yielded competitive results when evaluated on two second-generation deepfake datasets, namely Celeb-DFv2 and DFDC, while requiring only a fraction of the typical computational cost and resources.

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Section: Related Workmentioning

confidence: 99%