# UniShield: An Adaptive Multi-Agent Framework for Unified Forgery Image Detection and Localization arXiv: https://arxiv.org/abs/2510.03161 ยท HTML: https://mlsanity.com/paper/2510.03161/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 52 of its 86 references that are in the map. - First posted on arXiv 3 Oct 2025. ## Builds on (most cited first, 30 of 52) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021) - [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025) - [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023) - [Qwen-Image Technical Report](https://mlsanity.com/paper/2508.02324/) (2025) - [ShareGPT4V: Improving Large Multi-Modal Models with Better Captions](https://mlsanity.com/paper/2311.12793/) (2023) - [GLaMM: Pixel Grounding Large Multimodal Model](https://mlsanity.com/paper/2311.03356/) (2023) - [DIRE for Diffusion-Generated Image Detection](https://mlsanity.com/paper/2303.09295/) (2023) - [VideoAgent: Long-form Video Understanding with Large Language Model as Agent](https://mlsanity.com/paper/2403.10517/) (2024) - [A Sanity Check for AI-generated Image Detection](https://mlsanity.com/paper/2406.19435/) (2024) - [FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving](https://mlsanity.com/paper/2505.17685/) (2025) - [Hierarchical Fine-Grained Image Forgery Detection and Localization](https://mlsanity.com/paper/2303.17111/) (2023) - [PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2103.10596/) (2021) - [GenArtist: Multimodal LLM as an Agent for Unified Image Generation and Editing](https://mlsanity.com/paper/2407.05600/) (2024) - [DF40: Toward Next-Generation Deepfake Detection](https://mlsanity.com/paper/2406.13495/) (2024) - [PatchCraft: Exploring Texture Patch for Efficient AI-generated Image Detection](https://mlsanity.com/paper/2311.12397/) (2023) - [MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection](https://mlsanity.com/paper/2112.08935/) (2021) - [AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents](https://mlsanity.com/paper/2410.13825/) (2024) - [C2P-CLIP: Injecting Category Common Prompt in CLIP to Enhance Generalization in Deepfake Detection](https://mlsanity.com/paper/2408.09647/) (2024) - [FakeShield: Explainable Image Forgery Detection and Localization via Multi-modal Large Language Models](https://mlsanity.com/paper/2410.02761/) (2024) - [EditGuard: Versatile Image Watermarking for Tamper Localization and Copyright Protection](https://mlsanity.com/paper/2312.08883/) (2023) - [FrePGAN: Robust Deepfake Detection Using Frequency-level Perturbations](https://mlsanity.com/paper/2202.03347/) (2022) - [Spot the Fake: Large Multimodal Model-Based Synthetic Image Detection with Artifact Explanation](https://mlsanity.com/paper/2503.14905/) (2025) - [BianQue: Balancing the Questioning and Suggestion Ability of Health LLMs with Multi-turn Health Conversations Polished by ChatGPT](https://mlsanity.com/paper/2310.15896/) (2023) - [LEGION: Learning to Ground and Explain for Synthetic Image Detection](https://mlsanity.com/paper/2503.15264/) (2025) - [FFAA: Multimodal Large Language Model based Explainable Open-World Face Forgery Analysis Assistant](https://mlsanity.com/paper/2408.10072/) (2024) - [Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning](https://mlsanity.com/paper/2408.17065/) (2024) - [Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting](https://mlsanity.com/paper/2507.02939/) (2025) - [FreqBlender: Enhancing DeepFake Detection by Blending Frequency Knowledge](https://mlsanity.com/paper/2404.13872/) (2024) ## Built on by (most cited first, 9 of 9) - [Fake-HR1: Rethinking Reasoning of Vision Language Model for Synthetic Image Detection](https://mlsanity.com/paper/2602.10042/) (2026) - [Venus-DeFakerOne: Unified Fake Image Detection & Localization](https://mlsanity.com/paper/2605.14091/) (2026) - [EvoGuard: An Extensible Agentic RL-based Framework for Practical and Evolving AI-Generated Image Detection](https://mlsanity.com/paper/2603.17343/) (2026) - [OctoT2I: A Self-Evolving Agentic Text-to-Image Router](https://mlsanity.com/paper/2606.01803/) (2026) - [AgentFoX: LLM Agent-Guided Fusion with eXplainability for AI-Generated Image Detection](https://mlsanity.com/paper/2603.23115/) (2026) - [ReAlign: Generalizable Image Forgery Detection via Reasoning-Aligned Representation](https://mlsanity.com/paper/2605.16080/) (2026) - [SafeGuard: A Multi-Agent Perception-Reasoning Framework for Social-Risk AI-Generated Video Detection](https://mlsanity.com/paper/2607.03069/) (2026) - [HEDGE: Heterogeneous Ensemble for Detection of AI-GEnerated Images in the Wild](https://mlsanity.com/paper/2604.03555/) (2026) - [MOSAIC: Orchestrating Collaborative Knowledge Tracing with Hierarchical Semantic Alignment](https://mlsanity.com/paper/2606.29049/) (2026) ## Abstract (arXiv) With the rapid advancements in image generation, synthetic images have become increasingly realistic, posing significant societal risks, such as misinformation and fraud. Forgery Image Detection and Localization (FIDL) thus emerges as essential for maintaining information integrity and societal security. Despite impressive performances by existing domain-specific detection methods, their practical applicability remains limited, primarily due to their narrow specialization, poor cross-domain generalization, and the absence of an integrated adaptive framework. To address these issues, we propose UniShield, the novel multi-agent-based unified system capable of detecting and localizing image forgeries across diverse domains, including image manipulation, document manipulation, DeepFake, and AI-generated images. UniShield innovatively integrates a perception agent with a detection agent. The perception agent intelligently analyzes image features to dynamically select suitable detection models, while the detection agent consolidates various expert detectors into a unified framework and generates interpretable reports. Extensive experiments show that UniShield achieves state-of-the-art results, surpassing both existing unified approaches and domain-specific detectors, highlighting its superior practicality, adaptiveness, and scalability.