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LDFaceNet: latent diffusion-based network for high-fidelity deepfake generation

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dc.contributor.author Narang, Pratik
dc.date.accessioned 2025-05-08T06:55:07Z
dc.date.available 2025-05-08T06:55:07Z
dc.date.issued 2024-12
dc.identifier.uri https://link.springer.com/chapter/10.1007/978-3-031-78389-0_26
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/18883
dc.description.abstract Over the past decade, there has been tremendous progress in the domain of synthetic media generation. This is mainly due to the powerful methods based on generative adversarial networks (GANs). Very recently, diffusion probabilistic models, which are inspired by non-equilibrium thermodynamics, have taken the spotlight. In the realm of image generation, diffusion models (DMs) have exhibited remarkable proficiency in producing both realistic and heterogeneous imagery through their stochastic sampling procedure. This paper proposes a novel facial swapping module, termed as LDFaceNet (Latent Diffusion based Face Swapping Network), which is based on a guided latent diffusion model that utilizes facial segmentation and facial recognition modules for a conditioned denoising process. The model employs a unique loss function to offer directional guidance to the diffusion process. Notably, LDFaceNet can incorporate supplementary facial guidance for desired outcomes without any retraining. To the best of our knowledge, this represents the first application of the latent diffusion model in the face-swapping task without prior training. The results of this study demonstrate that the proposed method can generate extremely realistic and coherent images by leveraging the potential of the diffusion model for facial swapping, thereby yielding superior visual outcomes and greater diversity. en_US
dc.language.iso en en_US
dc.publisher Springer en_US
dc.subject Computer Science en_US
dc.subject Generative adversarial networks (GANs) en_US
dc.subject Image generation en_US
dc.subject Facial swapping en_US
dc.title LDFaceNet: latent diffusion-based network for high-fidelity deepfake generation en_US
dc.type Article en_US


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