A post-hoc detection framework exploits generation-induced patterns in autoregressive image outputs to enable provenance tracing across multiple IAR models without altering the generation process.
Statistical testing for efficient out of distribution detection in deep neural networks
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Introduces SCQ and P-TAMS for structure-adaptive conformal inference under pairwise exchangeability, claiming finite-sample FDR control for large-scale OOD testing.
DyMETER unifies hypernetwork-driven parameter adaptation and dynamic thresholding for online anomaly detection under concept drift.
citing papers explorer
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Data Provenance for Image Auto-Regressive Generation
A post-hoc detection framework exploits generation-induced patterns in autoregressive image outputs to enable provenance tracing across multiple IAR models without altering the generation process.
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Structure-Adaptive Conformal Inference for Large-Scale Out-of-Distribution Testing
Introduces SCQ and P-TAMS for structure-adaptive conformal inference under pairwise exchangeability, claiming finite-sample FDR control for large-scale OOD testing.
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Catching Every Ripple: Enhanced Anomaly Awareness via Dynamic Concept Adaptation
DyMETER unifies hypernetwork-driven parameter adaptation and dynamic thresholding for online anomaly detection under concept drift.