EIB-Net: Entropy-Guided Information Bottleneck for Generalizable AI-Generated Image Detection

arXiv:2609.29064 · cs.CV, cs.AI · Submitted 2026-09-24 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• Accuracy — 30× in this paper · explained in 1 episode
• Information Bottleneck — 20× in this paper
• Generalization — 19× in this paper · explained in 13 episodes
• Diffusion Model — 15× in this paper · explained in 8 episodes
• Image Entropy — 15× in this paper
• Regularization — 12× in this paper · explained in 3 episodes
• Diffusion Models — 11× in this paper · explained in 6 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• Generative Model — 7× in this paper · explained in 2 episodes
• Generative Models — 6× in this paper · explained in 2 episodes
• Robustness — 6× in this paper · explained in 5 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Variational Information Bottleneck — 6× in this paper
• Ablation — 5× in this paper · explained in 1 episode
• Consistency — 5× in this paper · explained in 3 episodes
• Entropy-Guided Patch Learning — 4× in this paper
• Trace — 4× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Jensen-Shannon Divergence — 3× in this paper
• Latent Representation — 3× in this paper · explained in 2 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Model-Agnostic — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Adversarial Robustness — 2× in this paper · explained in 3 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Reconstruction Error — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Generative Adversarial Networks (GANs) — 1× in this paper · explained in 3 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Self-consistency — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• GenDet: Towards Good Generalizations for AI-Generated Image Detection
• The information bottleneck method
• Deep Variational Information Bottleneck
• Diffusion Facial Forgery Detection
• LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
• Human Preference Score: Better Aligning Text-to-Image Models with Human Preference
• LoRA: Low-Rank Adaptation of Large Language Models
• VoxCeleb2: Deep Speaker Recognition

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