7 citations · 21 across the 8 of their papers we have counts for
9 papers
Too Emotional to Be Fake? Intra-Utterance Emotion Shifts Expose Failure Modes in Speech Deepfake Detectors
Jingyi Chen, Surya Koppisetti
Expressive speech synthesis can now change emotion within a single utterance, yet its impact on deepfake detection remains unclear. We introduce EmoShift, a benchmark comparing emo…
Alethia: A Foundational Encoder for Voice Deepfakes
Yi Zhu, Brahmi Dwivedi, Jayaram Raghuram +1
Existing voice deepfake detection and localization models rely heavily on representations extracted from speech foundation models (SFMs). However, downstream finetuning has now rea…
ICLAD: In-Context Learning with Comparison-Guidance for Audio Deepfake Detection
Benjamin Chou, Yi Zhu, Surya Koppisetti
Audio deepfakes pose a significant security threat, yet current state-of-the-art (SOTA) detection systems do not generalize well to realistic in-the-wild deepfakes. We introduce a…
A Data-Driven Diffusion-based Approach for Audio Deepfake Explanations
Petr Grinberg, Ankur Kumar, Surya Koppisetti +1
Evaluating explainability techniques, such as SHAP and LRP, in the context of audio deepfake detection is challenging due to lack of clear ground truth annotations. In the cases wh…
What Does an Audio Deepfake Detector Focus on? A Study in the Time Domain
Petr Grinberg, Ankur Kumar, Surya Koppisetti +1
Adding explanations to audio deepfake detection (ADD) models will boost their real-world application by providing insight on the decision making process. In this paper, we propose…
Learn from Real: Reality Defender's Submission to ASVspoof5 Challenge
Yi Zhu, Chirag Goel, Surya Koppisetti +3
Audio deepfake detection is crucial to combat the malicious use of AI-synthesized speech. Among many efforts undertaken by the community, the ASVspoof challenge has become one of t…
SLIM: Style-Linguistics Mismatch Model for Generalized Audio Deepfake Detection
Yi Zhu, Surya Koppisetti, Trang Tran +1
Audio deepfake detection (ADD) is crucial to combat the misuse of speech synthesized from generative AI models. Existing ADD models suffer from generalization issues, with a large…
Towards Attention-based Contrastive Learning for Audio Spoof Detection
Chirag Goel, Surya Koppisetti, Ben Colman +2
Vision transformers (ViT) have made substantial progress for classification tasks in computer vision. Recently, Gong et. al. '21, introduced attention-based modeling for several au…
AVFF: Audio-Visual Feature Fusion for Video Deepfake Detection
Trevine Oorloff, Surya Koppisetti, Nicolò Bonettini +5
With the rapid growth in deepfake video content, we require improved and generalizable methods to detect them. Most existing detection methods either use uni-modal cues or rely on…