FARE: Forensic Acceptance Region Estimation for Catching Bait-and-Switch Image Generators

arXiv:2609.30982 · cs.CV, cs.AI, cs.CR · Submitted 2026-09-25 · Read on arXiv

cs.CV, cs.AI, cs.CR

Submitted: 2026-09-25

Updated: 2026-09-25

Code: https://github.com/kaikaiyao/FARE

Terminology

• Verification — 37× in this paper · explained in 2 episodes
• Calibration — 35× in this paper · explained in 7 episodes
• Aggregation — 17× in this paper · explained in 1 episode
• Verifier — 17× in this paper · explained in 2 episodes
• Ablation — 12× in this paper · explained in 1 episode
• Stable Diffusion — 11× in this paper
• Pruning — 9× in this paper · explained in 4 episodes
• Quantization — 7× in this paper · explained in 12 episodes
• Trace — 6× in this paper · explained in 2 episodes
• Forensic Acceptance Region Estimation — 5× in this paper
• International Conference on Machine Learning — 5× in this paper
• Conference on Computer Vision and Pattern Recognition — 4× in this paper
• Benchmark — 3× in this paper · explained in 2 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Forensic Self-Description — 3× in this paper
• Generative AI — 3× in this paper · explained in 2 episodes
• Provenance — 3× in this paper · explained in 3 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Attack Success Rate (ASR) — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Fingerprinting — 2× in this paper · explained in 2 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 2× in this paper
• International Conference on Computer Vision — 2× in this paper
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Neighboring Pixel Relationships — 2× in this paper
• Ablation study — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Edinburgh International Data Facility — 1× in this paper
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• SynthID-Image: Image watermarking at internet scale

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