1 citations · 1 across the 5 of their papers we have counts for
6 papers
Time-Domain Voice Identity Morphing (TD-VIM): A Signal-Level Approach to Morphing Attacks on Speaker Verification Systems
Aravinda Reddy PN, Raghavendra Ramachandra, K. Sreenivasa Rao +2
In biometric systems, it is a common practice to associate each sample or template with a specific individual. Nevertheless, recent studies have demonstrated the feasibility of gen…
MorCode: Face Morphing Attack Generation using Generative Codebooks
Aravinda Reddy PN, Raghavendra Ramachandra, Sushma Venkatesh +3
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 gener…
NeuralMultiling: A Novel Neural Architecture Search for Smartphone based Multilingual Speaker Verification
Aravinda Reddy PN, Raghavendra Ramachandra, K. Sreenivasa Rao +1
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 B…
Straight Through Gumbel Softmax Estimator based Bimodal Neural Architecture Search for Audio-Visual Deepfake Detection
Aravinda Reddy PN, Raghavendra Ramachandra, Krothapalli Sreenivasa Rao +2
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 facia…
MLSD-GAN -- Generating Strong High Quality Face Morphing Attacks using Latent Semantic Disentanglement
Aravinda Reddy PN, Raghavendra Ramachandra, Krothapalli Sreenivasa Rao +1
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…
ExtSwap: Leveraging Extended Latent Mapper for Generating High Quality Face Swapping
Aravinda Reddy PN, K. Sreenivasa Rao, Raghavendra Ramachandra +1
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 in…