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Found 6 papers, 4 papers with code
Improved DeepFake Detection Using Whisper Features
With a recent influx of voice generation methods, the threat introduced by audio DeepFake (DF) is ever-increasing.
Defense Against Adversarial Attacks on Audio DeepFake Detection
Audio DeepFakes (DF) are artificially generated utterances created using deep learning, with the primary aim of fooling the listeners in a highly convincing manner.
SpecRNet: Towards Faster and More Accessible Audio DeepFake Detection
In this work, we focus on increasing accessibility to the audio DeepFake detection methods by providing SpecRNet, a neural network architecture characterized by a quick inference time and low computational requirements.
Attack Agnostic Dataset: Towards Generalization and Stabilization of Audio DeepFake Detection
Audio DeepFakes allow the creation of high-quality, convincing utterances and therefore pose a threat due to its potential applications such as impersonation or fake news.
A Note on Deepfake Detection with Low-Resources
Deepfakes are videos that include changes, quite often substituting face of a portrayed individual with a different face using neural networks.
Robust watermarking with double detector-discriminator approach
We also achieved 0. 90 bit accuracy for JPEG while recent methods provided at most 0. 83.
