DailyBench: A Unified Benchmark for AI-Generated and Manipulated Images from Modern Generative Models

arXiv:2607.24016 · cs.CV · Submitted 2026-07-27 · Read on arXiv

cs.CV

Submitted: 2026-07-27

Updated: 2026-09-22

Comments: Some errors have been fixed; please refer to the latest submitted version

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

Project page: https://dailybench.github.io

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 61× in this paper · explained in 1 episode
• Benchmark — 53× in this paper · explained in 2 episodes
• Robustness — 35× in this paper · explained in 5 episodes
• Generative Model — 26× in this paper · explained in 2 episodes
• Generative Models — 24× in this paper · explained in 3 episodes
• Generalization — 20× in this paper · explained in 13 episodes
• Fake-Preference Sampler — 10× in this paper
• Cascaded Compression Classifier — 6× in this paper
• Fidelity — 6× in this paper · explained in 2 episodes
• Trace — 6× in this paper · explained in 2 episodes
• Diffusion Model — 5× in this paper · explained in 10 episodes
• Fake-Preference Detector — 5× in this paper
• Ablation — 4× in this paper · explained in 1 episode
• Average Precision — 4× in this paper
• Diffusion Models — 4× in this paper · explained in 9 episodes
• Distribution Shift — 4× in this paper · explained in 9 episodes
• Generative AI — 4× in this paper · explained in 2 episodes
• Representation Learning — 4× in this paper · explained in 3 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Aggregation — 3× in this paper
• Consistency — 2× in this paper · explained in 3 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 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
• Alignment — 1× in this paper
• Benchmarking — 1× in this paper · explained in 4 episodes
• CCC with Dual-Pathway Feature — 1× in this paper
• Calibration — 1× in this paper · explained in 7 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• LoRA Fine-Tuning — 1× in this paper · explained in 3 episodes
• Provenance — 1× in this paper · explained in 5 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes

Sources

• Qwen3-VL Technical Report
• Large Scale GAN Training for High Fidelity Natural Image Synthesis
• Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer
• Real-Time Deepfake Detection in the Real-World
• UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild
• DINO-Detect: A Simple yet Effective Framework for Blur-Robust AI-Generated Image Detection
• DINOv3
• InstantID: Zero-shot Identity-Preserving Generation in Seconds
• Qwen-Image Technical Report
• Qwen3 Technical Report
• IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
• ReasonEdit: Towards Reasoning-Enhanced Image Editing Models
• PatchCraft: Exploring Texture Patch for Efficient AI-generated Image Detection

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models