5 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…
Post-hoc Selective Classification for Reliable Synthetic Image Detection
Kaixiang Zheng, Jacob H. Seidman
As synthetic images become increasingly realistic, reliable synthetic image detection techniques are of pressing need to prevent their misuse. Despite satisfactory in-distribution…
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…
CViT: Continuous Vision Transformer for Operator Learning
Sifan Wang, Jacob H Seidman, Shyam Sankaran +3
Operator learning, which aims to approximate maps between infinite-dimensional function spaces, is an important area in scientific machine learning with applications across various…
Score Neural Operator: A Generative Model for Learning and Generalizing Across Multiple Probability Distributions
Xinyu Liao, Aoyang Qin, Jacob Seidman +3
Most existing generative models are limited to learning a single probability distribution from the training data and cannot generalize to novel distributions for unseen data. An ar…