ReAlign: Generalizable Image Forgery Detection via Reasoning-Aligned Representation

arXiv:2605.16080 · cs.CV · Submitted 2026-05-15 · Read on arXiv

cs.CV

Submitted: 2026-05-15

Updated: 2026-10-08

Code: https://github.com/Deep-Agent/R1-V

Terminology

• Generalization — 30× in this paper · explained in 13 episodes
• Accuracy — 16× in this paper · explained in 1 episode
• Alignment — 16× in this paper · explained in 1 episode
• GRPO — 14× in this paper · explained in 6 episodes
• Reinforcement Learning — 10× in this paper · explained in 4 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• Generative Model — 8× in this paper · explained in 2 episodes
• Generative Models — 8× in this paper · explained in 2 episodes
• Contrastive Learning — 7× in this paper · explained in 5 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Semantic Understanding — 5× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Ablation study — 4× in this paper · explained in 1 episode
• Fidelity — 4× in this paper · explained in 2 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Diffusion Model — 3× in this paper · explained in 8 episodes
• Embeddings — 3× 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 Models — 2× in this paper · explained in 6 episodes
• Explainability — 2× in this paper · explained in 3 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Group Relative Policy Optimization — 2× in this paper
• IEEE International Conference on Image Processing — 2× in this paper
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Reward Function — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• Winter Conference on Applications of Computer Vision — 2× in this paper
• Adaptability — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Error Decomposition — 1× in this paper · explained in 3 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Federated Learning — 1× in this paper · explained in 14 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• LoRA Fine-Tuning — 1× in this paper · explained in 3 episodes
• Multi-Agent Framework — 1× in this paper · explained in 3 episodes
• Non-IID Data — 1× in this paper · explained in 2 episodes
• Proximal Policy Optimization — 1× in this paper
• Proximal Policy Optimization (PPO) — 1× in this paper · explained in 4 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Qwen2.5-VL Technical Report
• AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors
• ShareGPT4V: Improving Large Multi-Modal Models with Better Captions
• SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
• ForensicHub: A Unified Benchmark & Codebase for All-Domain Fake Image Detection and Localization
• Seedream 3.0 Technical Report
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• UniShield: An Adaptive Multi-Agent Framework for Unified Forgery Image Detection and Localization
• So-Fake: Benchmarking and Explaining Social Media Image Forgery Detection
• LEGION: Learning to Ground and Explain for Synthetic Image Detection
• Seeing Before Reasoning: A Unified Framework for Generalizable and Explainable Fake Image Detection
• ForgeryGPT: A Multimodal LLM for Interpretable Image Forgery Detection and Localization
• IML-ViT: Benchmarking Image Manipulation Localization by Vision Transformer
• TextSleuth: Towards Explainable Tampered Text Detection
• VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model
• GPT-4 Technical Report
• Qwen-Image Technical Report
• AvatarShield: Visual Reinforcement Learning for Human-Centric Synthetic Video Detection
• A Sanity Check for AI-generated Image Detection

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