[2606.01885] Divide and Conquer: Reliable Multi-View Evidential Learning for Deepfake Detection
Abstract page for arXiv paper 2606.01885: Divide and Conquer: Reliable Multi-View Evidential Learning for Deepfake Detection
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Abstract page for arXiv paper 2606.01885: Divide and Conquer: Reliable Multi-View Evidential Learning for Deepfake Detection
Abstract page for arXiv paper 2606.01256: Distribution-free changepoint localization after sequential change detection
Abstract page for arXiv paper 2605.31579: Functional Multi-Target Detection via Bispectrum Inversion
Abstract page for arXiv paper 2605.30416: A Bandpass Axion Or: How I Learned To Stop Worrying About Stars And Love The Lab
Abstract page for arXiv paper 2605.31113: TSM-Bench: Detecting LLM-Generated Text in Real-World Wikipedia Editing Practices
Behavioral analysis is an interesting approach to detect bots. It surely is not the panacea for bot detection, but it certainly is an useful extension in your b...
While there are some broad patterns to how AI writes, it’s still very easy to mold AI content for your own purposes.
Looks closely and you’ll see that everything is equally off.
Abstract page for arXiv paper 2502.08695: A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of Distribution Detection
Background: Health care workers (HCWs) face sustained psychological demands that place them at heightened risk for burnout and posttraumatic stress disorder (PT...