From Evidence to Verdict: An Agent-Based Forensic Framework for AI-Generated Image Detection

• URL: http://arxiv.org/abs/2511.00181v1
• Date: Fri, 31 Oct 2025 18:36:49 GMT
• Title: From Evidence to Verdict: An Agent-Based Forensic Framework for AI-Generated Image Detection
• Authors: Mengfei Liang, Yiting Qu, Yukun Jiang, Michael Backes, Yang Zhang,
• Abstract summary: We introduce AIFo (Agent-based Image Forensics), a training-free framework that emulates human forensic investigation through multi-agent collaboration.<n>Unlike conventional methods, our framework employs a set of forensic tools, including reverse image search, metadata extraction, pre-trained classifiers, and VLM analysis.<n>Our comprehensive evaluation spans 6,000 images and challenges real-world scenarios, including images from modern generative platforms and diverse online sources.
• Score: 19.240335260177382
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The rapid evolution of AI-generated images poses unprecedented challenges to information integrity and media authenticity. Existing detection approaches suffer from fundamental limitations: traditional classifiers lack interpretability and fail to generalize across evolving generative models, while vision-language models (VLMs), despite their promise, remain constrained to single-shot analysis and pixel-level reasoning. To address these challenges, we introduce AIFo (Agent-based Image Forensics), a novel training-free framework that emulates human forensic investigation through multi-agent collaboration. Unlike conventional methods, our framework employs a set of forensic tools, including reverse image search, metadata extraction, pre-trained classifiers, and VLM analysis, coordinated by specialized LLM-based agents that collect, synthesize, and reason over cross-source evidence. When evidence is conflicting or insufficient, a structured multi-agent debate mechanism allows agents to exchange arguments and reach a reliable conclusion. Furthermore, we enhance the framework with a memory-augmented reasoning module that learns from historical cases to improve future detection accuracy. Our comprehensive evaluation spans 6,000 images across both controlled laboratory settings and challenging real-world scenarios, including images from modern generative platforms and diverse online sources. AIFo achieves 97.05% accuracy, substantially outperforming traditional classifiers and state-of-the-art VLMs. These results demonstrate that agent-based procedural reasoning offers a new paradigm for more robust, interpretable, and adaptable AI-generated image detection.

Related papers

• DeepImageSearch: Benchmarking Multimodal Agents for Context-Aware Image Retrieval in Visual Histories [52.57197752244638]
  We introduce DeepImageSearch, a novel agentic paradigm that reformulates image retrieval as an autonomous exploration task.<n>Models must plan and perform multi-step reasoning over raw visual histories to locate targets based on implicit contextual cues.<n>We construct DISBench, a challenging benchmark built on interconnected visual data.
  arXiv  Detail & Related papers  (2026-02-11T12:51:10Z)
• Code-in-the-Loop Forensics: Agentic Tool Use for Image Forgery Detection [59.04089915447622]
  ForenAgent is an interactive IFD framework that enables MLLMs to autonomously generate, execute, and refine Python-based low-level tools around the detection objective.<n>Inspired by human reasoning, we design a dynamic reasoning loop comprising global perception, local focusing, iterative probing, and holistic adjudication.<n>Experiments show that ForenAgent exhibits emergent tool-use competence and reflective reasoning on challenging IFD tasks.
  arXiv  Detail & Related papers  (2025-12-18T08:38:44Z)
• Self-Supervised AI-Generated Image Detection: A Camera Metadata Perspective [80.10217707456046]
  We introduce a self-supervised approach for detecting AI-generated images that leverages camera metadata.<n>We train a feature extractor solely on camera-captured photographs by classifying categorical EXIF tags.<n>Our detectors deliver strong generalization to in-the-wild samples and robustness to common benign image perturbations.
  arXiv  Detail & Related papers  (2025-12-05T11:53:18Z)
• Provenance of AI-Generated Images: A Vector Similarity and Blockchain-based Approach [3.632189127068905]
  We propose an embedding-based AI image detection framework to distinguish AI-generated images from real (human-created) ones.<n>Our methodology is built on the hypothesis that AI-generated images demonstrate closer embedding proximity to other AI-generated content.<n>Our results confirm that moderate to high perturbations minimally impact the embedding signatures, with perturbed images maintaining close similarity matches to their original versions.
  arXiv  Detail & Related papers  (2025-10-15T00:49:56Z)
• ThinkFake: Reasoning in Multimodal Large Language Models for AI-Generated Image Detection [51.93101033997245]
  Increasing realism of AI-generated images has raised serious concerns about misinformation and privacy violations.<n>We propose ThinkFake, a novel reasoning-based and generalizable framework for AI-generated image detection.<n>We show that ThinkFake outperforms state-of-the-art methods on the GenImage benchmark and demonstrates strong zero-shot generalization on the challenging LOKI benchmark.
  arXiv  Detail & Related papers  (2025-09-24T07:34:09Z)
• Semantic-Aware Reconstruction Error for Detecting AI-Generated Images [22.83053631078616]
  We propose a novel representation, namely Semantic-Aware Reconstruction Error (SARE), that measures the semantic difference between an image and its caption-guided reconstruction.<n>SARE provides a robust and discriminative feature for detecting fake images across diverse generative models.<n>We also introduce a fusion module that integrates SARE into the backbone detector via a cross-attention mechanism.
  arXiv  Detail & Related papers  (2025-08-13T04:37:36Z)
• ForensicsSAM: Toward Robust and Unified Image Forgery Detection and Localization Resisting to Adversarial Attack [56.0056378072843]
  We show that highly transferable adversarial images can be crafted solely via the upstream model.<n>We propose ForensicsSAM, a unified IFDL framework with built-in adversarial robustness.
  arXiv  Detail & Related papers  (2025-08-10T16:03:44Z)
• ForenX: Towards Explainable AI-Generated Image Detection with Multimodal Large Language Models [82.04858317800097]
  We present ForenX, a novel method that not only identifies the authenticity of images but also provides explanations that resonate with human thoughts.<n>ForenX employs the powerful multimodal large language models (MLLMs) to analyze and interpret forensic cues.<n>We introduce ForgReason, a dataset dedicated to descriptions of forgery evidences in AI-generated images.
  arXiv  Detail & Related papers  (2025-08-02T15:21:26Z)
• FakeScope: Large Multimodal Expert Model for Transparent AI-Generated Image Forensics [66.14786900470158]
  We propose FakeScope, an expert multimodal model (LMM) tailored for AI-generated image forensics.<n>FakeScope identifies AI-synthetic images with high accuracy and provides rich, interpretable, and query-driven forensic insights.<n>FakeScope achieves state-of-the-art performance in both closed-ended and open-ended forensic scenarios.
  arXiv  Detail & Related papers  (2025-03-31T16:12:48Z)
• Towards Generalizable Forgery Detection and Reasoning [23.858913560970866]
  We formulate detection and explanation as a unified Forgery Detection and Reasoning task (FDR-Task)<n>We introduce the Multi-Modal Forgery Reasoning dataset (MMFR-Dataset), a large-scale dataset containing 120K images across 10 generative models, with 378K reasoning annotations on forgery attributes.<n>Experiments across multiple generative models demonstrate that FakeReasoning achieves robust generalization and outperforms state-of-the-art methods on both detection and reasoning tasks.
  arXiv  Detail & Related papers  (2025-03-27T06:54:06Z)
• Methods and Trends in Detecting AI-Generated Images: A Comprehensive Review [0.17188280334580194]
  Generative Adversarial Networks (GANs), Diffusion Models, and Variational Autoencoders (VAEs) have enabled the synthesis of high-quality multimedia data.<n>These advancements have also raised significant concerns regarding adversarial attacks, unethical usage, and societal harm.<n>This survey provides a comprehensive review of state-of-the-art techniques for detecting and classifying synthetic images generated by advanced generative AI models.
  arXiv  Detail & Related papers  (2025-02-21T03:16:18Z)
• Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models [103.64435911083432]
  We present a novel contrastive learning strategy called it Proactive Pseudo-Intervention (PPI) PPI leverages proactive interventions to guard against image features with no causal relevance. We also devise a novel causally informed salience mapping module to identify key image pixels to intervene, and show it greatly facilitates model interpretability.
  arXiv  Detail & Related papers  (2020-12-06T20:30:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.