ClueAegis: Heuristic-to-Reasoning Cognitive-skill Learning for Unified Evidence-based Synthetic Image Detection

• URL: http://arxiv.org/abs/2605.25009v1
• Date: Sun, 24 May 2026 11:26:15 GMT
• Title: ClueAegis: Heuristic-to-Reasoning Cognitive-skill Learning for Unified Evidence-based Synthetic Image Detection
• Abstract summary: Existing methods are often limited to end-to-end classification or monolithic reasoning.<n>We propose a cognitive skill learning framework for evidence-based forensic analysis.<n>We show that ClueAegis achieves state-of-the-art performance while improving cross-domain and robustness.
• Score: 25.871829081625496
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid advancement of generative models has made synthetic images increasingly realistic, challenging reliable detection. Existing methods are often limited to end-to-end classification or monolithic reasoning, and thus fail to model structured forensic reasoning and heterogeneous visual evidence. We revisit synthetic image detection from a cognitive perspective and propose a \textit{Heuristic-to-Reasoning} cognitive skill learning framework for evidence-based forensic analysis. Given an input image, our framework first extracts heuristic perceptual clues, selects the optimal forensic skill, and then performs skill-conditioned reasoning for evidence extraction and decision making. To support this paradigm, we introduce \textbf{ClueAegis-Bench}, which decomposes synthetic image detection into explicitly annotated forensic cognitive skills for structured evaluation beyond binary classification. Based on this benchmark, we propose \textbf{ClueAegis} (\underline{C}ognitive-skill \underline{L}earning for \underline{U}nified \underline{E}vidence-based Synthetic Image Detection), a two-stage agentic framework that conducts heuristic skill selection followed by evidence-guided reasoning through skill-conditioned toolchains. This design reformulates synthetic image detection as a configurable multi-skill reasoning process that bridges perception, skill selection, and forensic reasoning. Extensive experiments show that ClueAegis achieves state-of-the-art performance while improving cross-domain generalization and robustness. It also provides transparent reasoning trajectories and structured forensic evidence, offering a more explainable alternative to conventional end-to-end detectors.

Related papers

• Primitive-Driven Compositional Forensic Visual Prompting for Open-World Face Anti-Spoofing [59.765442274924595]
  We propose a compositional visual prompt learning framework that operates entirely in the visual feature space.<n>The framework employs patch-aware attention to refine a shared set of learnable micro-forensic primitives into localized forensic evidence units.<n>Experiments on nine open-world protocols demonstrate state-of-the-art performance, strong cross-domain generalization, and robust adaptation to unseen attacks.
  arXiv  Detail & Related papers  (2026-08-18T04:18:08Z)
• ForensicConcept: Transferable Forensic Concepts for AIGI Detection [81.29606528968698]
  We propose ForensicConcept, a framework that extracts explicit forensic concepts from detectors and enables their transfer across backbones.<n>Our method localizes decision-critical patches via Transformer attribution, clusters them into a compact concept codebook, and uses a concept-aligned projection to produce auditable evidence readouts.
  arXiv  Detail & Related papers  (2026-06-05T08:25:34Z)
• Detection of Hate and Threat in Digital Forensics: A Case-Driven Multimodal Approach [0.0]
  This paper presents a case-driven multimodal approach for hate and threat detection in forensic analysis.<n>The proposed framework explicitly determines the presence and source of textual evidence, distinguishing between embedded text, associated contextual text, and image-only evidence.<n>By conditioning inference on evidence availability, the approach mirrors forensic decision-making, improves evidentiary traceability, and avoids unjustified modality assumptions.
  arXiv  Detail & Related papers  (2026-04-08T21:50:02Z)
• AgentFoX: LLM Agent-Guided Fusion with eXplainability for AI-Generated Image Detection [21.02452809723292]
  We present textbfAgentFoX, a framework that redefines AIGI detection as a dynamic, multi-phase analytical process.<n>Our approach employs a quick-integration fusion mechanism guided by a curated knowledge base.<n>AgentFoX produces a detailed, human-readable forensic report that substantiates its verdict.
  arXiv  Detail & Related papers  (2026-03-24T12:10:21Z)
• Unsupervised Synthetic Image Attribution: Alignment and Disentanglement [55.853285140682665]
  We propose a simple yet effective unsupervised method called Alignment and Disentanglement.<n>Specifically, we begin by performing basic concept alignment using contrastive self-supervised learning.<n>Next, we enhance the model's attribution ability by promoting representation disentanglement with the Infomax loss.
  arXiv  Detail & Related papers  (2026-01-30T07:31:53Z)
• REVEAL: Reasoning-enhanced Forensic Evidence Analysis for Explainable AI-generated Image Detection [30.963994372913092]
  We introduce textbfREVEAL-Bench, the first reasoning-enhanced multimodal benchmark for AI-generated image detection.<n>Our framework integrates detection with a novel expert-grounded reinforcement learning.<n> REVEAL significantly enhances detection accuracy, explanation fidelity, and robust cross-model generalization.
  arXiv  Detail & Related papers  (2025-11-28T13:11:08Z)
• From Evidence to Verdict: An Agent-Based Forensic Framework for AI-Generated Image Detection [19.240335260177382]
  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.
  arXiv  Detail & Related papers  (2025-10-31T18:36:49Z)
• 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)
• Propose and Rectify: A Forensics-Driven MLLM Framework for Image Manipulation Localization [49.71303998618939]
  This paper presents a novel Propose-Rectify framework that bridges semantic reasoning with forensic-specific analysis.<n>Our framework ensures that initial semantic proposals are systematically validated and enhanced through concrete technical evidence, resulting in comprehensive detection accuracy and localization precision.
  arXiv  Detail & Related papers  (2025-08-25T12:43:53Z)
• 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)
• Natias: Neuron Attribution based Transferable Image Adversarial Steganography [62.906821876314275]
  adversarial steganography has garnered considerable attention due to its ability to effectively deceive deep-learning-based steganalysis. We propose a novel adversarial steganographic scheme named Natias. Our proposed method can be seamlessly integrated with existing adversarial steganography frameworks.
  arXiv  Detail & Related papers  (2024-09-08T04:09:51Z)
• A Principled Design of Image Representation: Towards Forensic Tasks [75.40968680537544]
  We investigate the forensic-oriented image representation as a distinct problem, from the perspectives of theory, implementation, and application. At the theoretical level, we propose a new representation framework for forensics, called Dense Invariant Representation (DIR), which is characterized by stable description with mathematical guarantees. We demonstrate the above arguments on the dense-domain pattern detection and matching experiments, providing comparison results with state-of-the-art descriptors.
  arXiv  Detail & Related papers  (2022-03-02T07:46:52Z)

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.