Typographic Attack Against VLM-based AI-generated Image Detection

arXiv:2609.39662 · cs.CV · Submitted 2026-09-30 · Read on arXiv

cs.CV

Submitted: 2026-09-30

Updated: 2026-09-30

Terminology

• Accuracy — 10× in this paper · explained in 1 episode
• Robustness — 9× in this paper · explained in 5 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Provenance — 3× in this paper · explained in 3 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Vision-Language Models (VLMs) — 2× in this paper · explained in 6 episodes
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Attack Success Rate (ASR) — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• On-Policy Distillation — 1× in this paper · explained in 3 episodes

Sources

• BusterX++: Towards Unified Cross-Modal AI-Generated Content Detection and Explanation with MLLM
• Veritas++: Value-aware On-Policy Distillation for Perception-Enhanced AIGI Detection
• DRIFT: Derailing Denoising Trajectories of Flow-Matching VLAs with Adversarial Patch Attack
• Vision-LLMs Can Fool Themselves with Self-Generated Typographic Attacks
• Beyond Pixels: Semantic-aware Typographic Attack for Geo-Privacy Protection
• Nearly Solved? Robust Deepfake Detection Requires More than Visual Forensics
• GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
• Qwen3.5-Omni Technical Report

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