REALIS: A Curated Dataset for Studying the Challenges of AI Image Detection

arXiv:2609.32734 · cs.CV, cs.AI · Submitted 2026-09-26 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/black-forest-labs/flux

Terminology

• AUC — 86× in this paper · explained in 2 episodes
• Benchmark — 31× in this paper · explained in 2 episodes
• Robustness — 19× in this paper · explained in 5 episodes
• Generalization — 12× in this paper · explained in 13 episodes
• Conference on Computer Vision and Pattern Recognition — 11× in this paper
• Transformer — 8× in this paper · explained in 2 episodes
• Diffusion Model — 6× in this paper · explained in 8 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Benchmarking — 4× in this paper · explained in 3 episodes
• Diffusion Models — 4× in this paper · explained in 6 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Trace — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Adversarial Attacks — 2× in this paper · explained in 1 episode
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Quantization — 2× in this paper · explained in 12 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Influence Functions — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 4 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• Kandinsky 3.0 Technical Report
• Kandinsky 5.0: A Family of Foundation Models for Image and Video Generation
• FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
• HiDream-I1: A High-Efficient Image Generative Foundation Model with Sparse Diffusion Transformer
• Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling
• DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysis
• Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation
• SDXL-Lightning: Progressive Adversarial Diffusion Distillation
• A Comprehensive Dataset for Human vs. AI Generated Image Detection
• Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference
• From Pixels to Prose: A Large Dataset of Dense Image Captions
• Pixel Seal: Adversarial-only training for invisible image and video watermarking
• Ovis-Image Technical Report
• Qwen-Image Technical Report
• OmniGen2: Towards Instruction-Aligned Multimodal Generation
• Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer

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