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Found 5 papers, 2 papers with code
Deep Learning-based Group Causal Inference in Multivariate Time-series
Causal inference in a nonlinear system of multivariate timeseries is instrumental in disentangling the intricate web of relationships among variables, enabling us to make more accurate predictions and gain deeper insights into real-world complex systems.
CapST: An Enhanced and Lightweight Model Attribution Approach for Synthetic Videos
Deepfake videos, generated through AI faceswapping techniques, have garnered considerable attention due to their potential for powerful impersonation attacks.
Multimodal Forgery Detection Using Ensemble Learning
The recent rapid revolution in Artificial Intelligence (AI) technology has enabled the creation of hyper-realistic deepfakes, and detecting deepfake videos (also known as AIsynthesized videos) has become a critical task.
Ranked #1 on
Multimodal Forgery Detection
on FakeAVCeleb
(using extra training data)
Causal Discovery using Model Invariance through Knockoff Interventions
Cause-effect analysis is crucial to understand the underlying mechanism of a system.
Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals
Overall our method outperforms the widely used vector autoregressive Granger causality and PCMCI in detecting nonlinear causal dependency in multivariate time series.
