5 accepted papers
The audio forensics field has recently faced a new challenge: singing voice deepfake detection. Current approaches to tackle this problem have borrowed methods initially developed for the more established task of speech deepfake detection, often simply retraining these systems on singing voice data.…
In speech deepfake detection, one of the critical aspects is developing detectors able to generalize on unseen data and distinguish fake signals across different datasets. Common approaches to this challenge involve incorporating diverse data into the training process or fine-tuning models on unseen…
Speech deepfakes pose a significant threat to personal security and content authenticity. Several detectors have been proposed in the literature, and one of the primary challenges these systems have to face is the generalization over unseen data to identify fake signals across a wide range of datase…
Recent advances in speech synthesis and counterfeit audio generation have pushed the multimedia forensics community to develop speech deepfake detection techniques to avoid threats and unpleasant situations. Although synthetic speech detectors show excellent performance in controlled conditions, the…
In recent years, audio and video deepfake technology has advanced relentlessly, severely impacting people’s reputation and reliability. Several factors have facilitated the growing deepfake threat. On the one hand, the hyper-connected society of social and mass media enables the spread of multimedia…