7 citations · 15 across the 9 of their papers we have counts for
10 papers
FoeGlass: Simple In-Context Learning Is Enough for Red Teaming Audio Deepfake Detectors
Sepehr Dehdashtian, Jacob H Seidman, Vishnu N Boddeti +1
Audio deepfake detection (ADD) models are critical for countering the malicious use of text-to-speech (TTS) models. Evaluating and strengthening ADD models requires developing data…
PolyJuice Makes It Real: Black-Box, Universal Red Teaming for Synthetic Image Detectors
Sepehr Dehdashtian, Mashrur M. Morshed, Jacob H. Seidman +2
Synthetic image detectors (SIDs) are a key defense against the risks posed by the growing realism of images from text-to-image (T2I) models. Red teaming improves SID's effectivenes…
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…
X-Edit: Detecting and Localizing Edits in Images Altered by Text-Guided Diffusion Models
Valentina Bazyleva, Nicolo Bonettini, Gaurav Bharaj
Text-guided diffusion models have significantly advanced image editing, enabling highly realistic and local modifications based on textual prompts. While these developments expand…
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…
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…
Common Sense Reasoning for Deepfake Detection
Yue Zhang, Ben Colman, Xiao Guo +2
State-of-the-art deepfake detection approaches rely on image-based features extracted via neural networks. While these approaches trained in a supervised manner extract likely fake…
Common-Sense Bias Modeling for Classification Tasks
Miao Zhang, Zee fryer, Ben Colman +2
Machine learning model bias can arise from dataset composition: correlated sensitive features can distort the downstream classification model's decision boundary and lead to perfor…