WaveFake: A Data Set to Facilitate Audio Deepfake Detection
Deep generative modeling has the potential to cause significant harm to society. Recognizing this threat, a magnitude of research into detecting so-called "Deepfakes" has emerged. This research most often focuses on the image domain, while studies exploring generated audio signals have, so-far, been neglected. In this paper we make three key contributions to narrow this gap. First, we provide researchers with an introduction to common signal processing techniques used for analyzing audio signals. Second, we present a novel data set, for which we collected nine sample sets from five different network architectures, spanning two languages. Finally, we supply practitioners with two baseline models, adopted from the signal processing community, to facilitate further research in this area.
Code (2)
Tasks
Audio Deepfake DetectionDeepFake DetectionFace SwappingSimilar Papers 제목 키워드 기반
Towards generalizing deep-audio fake detection networks
Today's generative neural networks allow the creation of high-quality synthetic speech at scale. While we welcome the creative use of this new technology, we must also recognize the risks. As synthetic speech is abused f…
Face SwappingAFSS: Artifact-Focused Self-Synthesis for Mitigating Bias in Audio Deepfake Detection
The rapid advancement of generative models has enabled highly realistic audio deepfakes, yet current detectors suffer from a critical bias problem, leading to poor generalization across unseen datasets. This paper propos…
Audio Deepfake DetectionContextual Cross-Modal Attention for Audio-Visual Deepfake Detection and Localization
In the digital age, the emergence of deepfakes and synthetic media presents a significant threat to societal and political integrity. Deepfakes based on multi-modal manipulation, such as audio-visual, are more realistic …
DeepFake DetectionFace SwappingDoes Current Deepfake Audio Detection Model Effectively Detect ALM-based Deepfake Audio?
Currently, Audio Language Models (ALMs) are rapidly advancing due to the developments in large language models and audio neural codecs. These ALMs have significantly lowered the barrier to creating deepfake audio, genera…
Audio Deepfake DetectionDeepFake DetectionFace SwappingInvestigating the Viability of Employing Multi-modal Large Language Models in the Context of Audio Deepfake Detection
While Vision-Language Models (VLMs) and Multimodal Large Language Models (MLLMs) have shown strong generalisation in detecting image and video deepfakes, their use for audio deepfake detection remains largely unexplored.…
Audio Deepfake Detection