June 10, 2024, 4:46 a.m. | Lanzino Romeo, Fontana Federico, Diko Anxhelo, Marini Marco Raoul, Cinque Luigi

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.04932v1 Announce Type: cross
Abstract: Deepfake detection aims to contrast the spread of deep-generated media that undermines trust in online content. While existing methods focus on large and complex models, the need for real-time detection demands greater efficiency. With this in mind, unlike previous work, we introduce a novel deepfake detection approach on images using Binary Neural Networks (BNNs) for fast inference with minimal accuracy loss. Moreover, our method incorporates Fast Fourier Transform (FFT) and Local Binary Pattern (LBP) as …

abstract arxiv binary contrast cs.cv cs.lg deepfake detection efficiency faster focus generated lies media mind networks neural networks novel online content real-time trust type while work

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