Introduces Forged Calamity benchmark and shows that fine-tuned and zero-shot synthetic image detectors lose substantial accuracy on unseen generators and disaster types.
Leveraging frequency analysis for deep fake image recognition.arXiv preprint arXiv:2003.08685
4 Pith papers cite this work, alongside 209 external citations. Polarity classification is still indexing.
4
Pith papers citing it
209
external citations · external index
citation-role summary
background 4
citation-polarity summary
fields
cs.CV 4years
2026 4roles
background 3polarities
background 3representative citing papers
ANL uses diffusion noise prediction and attention to regularize deepfake detectors for better generalization to unseen synthesis methods without added inference cost.
SPECTRA-Net fuses multi-view tensor representations from vision foundation models, spectral analysis, local anomaly detection, and statistical descriptors to achieve state-of-the-art cross-domain AI-generated image detection with explainable artifact localization.
citing papers explorer
-
Forged Calamity: Benchmark for Cross-Domain Synthetic Disaster Detection in the Age of Diffusion
Introduces Forged Calamity benchmark and shows that fine-tuned and zero-shot synthetic image detectors lose substantial accuracy on unseen generators and disaster types.
-
Deepfake Detection Generalization with Diffusion Noise
ANL uses diffusion noise prediction and attention to regularize deepfake detectors for better generalization to unseen synthesis methods without added inference cost.
-
SPECTRA-Net: Scalable Pipeline for Explainable Cross-domain Tensor Representations for AI-generated Images Detection
SPECTRA-Net fuses multi-view tensor representations from vision foundation models, spectral analysis, local anomaly detection, and statistical descriptors to achieve state-of-the-art cross-domain AI-generated image detection with explainable artifact localization.
- DVAR: Adversarial Multi-Agent Debate for Video Authenticity Detection