Zobrazeno 1 - 10
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pro vyhledávání: '"Rao, A. Sreenivasa"'
Autor:
PN, Aravinda Reddy, Ramachandra, Raghavendra, Venkatesh, Sushma, Rao, Krothapalli Sreenivasa, Mitra, Pabitra, Krishna, Rakesh
Face recognition systems (FRS) can be compromised by face morphing attacks, which blend textural and geometric information from multiple facial images. The rapid evolution of generative AI, especially Generative Adversarial Networks (GAN) or Diffusio
Externí odkaz:
http://arxiv.org/abs/2410.07625
Gumbel Rao Monte Carlo based Bi-Modal Neural Architecture Search for Audio-Visual Deepfake Detection
Autor:
PN, Aravinda Reddy, Ramachandra, Raghavendra, Rao, Krothapalli Sreenivasa, Rathod, Pabitra Mitra Vinod
Deepfakes pose a critical threat to biometric authentication systems by generating highly realistic synthetic media. Existing multimodal deepfake detectors often struggle to adapt to diverse data and rely on simple fusion methods. To address these ch
Externí odkaz:
http://arxiv.org/abs/2410.06543
Multilingual speaker verification introduces the challenge of verifying a speaker in multiple languages. Existing systems were built using i-vector/x-vector approaches along with Bi-LSTMs, which were trained to discriminate speakers, irrespective of
Externí odkaz:
http://arxiv.org/abs/2408.04362
Autor:
PN, Aravinda Reddy, Ramachandra, Raghavendra, Rao, Krothapalli Sreenivasa, Mitra, Pabitra, Rathod, Vinod
Deepfakes are a major security risk for biometric authentication. This technology creates realistic fake videos that can impersonate real people, fooling systems that rely on facial features and voice patterns for identification. Existing multimodal
Externí odkaz:
http://arxiv.org/abs/2406.13384
Face-morphing attacks are a growing concern for biometric researchers, as they can be used to fool face recognition systems (FRS). These attacks can be generated at the image level (supervised) or representation level (unsupervised). Previous unsuper
Externí odkaz:
http://arxiv.org/abs/2404.12679
Publikováno v:
ACM Trans. Intell. Syst. Technol. 11, 6, Article 69 (December 2020), pages 69:1-69:28
Over the past few years, automation of outfit composition has gained much attention from the research community. Most of the existing outfit recommendation systems focus on pairwise item compatibility prediction (using visual and text features) to sc
Externí odkaz:
http://arxiv.org/abs/2402.16660
Video lectures are becoming more popular and in demand as online classroom teaching is becoming more prevalent. Massive Open Online Courses (MOOCs), such as NPTEL, have been creating high-quality educational content that is freely accessible to stude
Externí odkaz:
http://arxiv.org/abs/2401.01356
We present a novel face swapping method using the progressively growing structure of a pre-trained StyleGAN. Previous methods use different encoder decoder structures, embedding integration networks to produce high-quality results, but their quality
Externí odkaz:
http://arxiv.org/abs/2310.12736
Autor:
Raju, V.V. Narasimha, Saravanakumar, R., Yusuf, Nadia, Pradhan, Rahul, Hamdi, Hedi, Saravanan, K. Aanandha, Rao, Vuda Sreenivasa, Askar, Majid A.
Publikováno v:
In Alexandria Engineering Journal December 2024 108:498-508
Autor:
Malempati, Sravanthi, Agrawal, Neelam, Ravisankar, Devalaraju, Kothapalli, Venkata Sai Rahul Trivedi, Cheemanapalli, Srinivasulu, Tamanam, Raghava Rao, Govatati, Suresh, Rao, Pasupuleti Sreenivasa
Publikováno v:
In Human Gene December 2024 42