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arxiv: 2605.16080 · v1 · pith:JSIQAZ2Ynew · submitted 2026-05-15 · 💻 cs.CV

ReAlign: Generalizable Image Forgery Detection via Reasoning-Aligned Representation

Qing Huang , Zhipei Xu , Xuanyu Zhang , Xiangyu Yu , Jian Zhang This is my paper

Pith reviewed 2026-05-20 19:26 UTC · model grok-4.3

classification 💻 cs.CV
keywords image forgery detectionAI generated imagescontrastive learningLLM reasoninggeneralizable detectionlightweight model
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The pith

Aligning visual features with LLM reasoning texts creates a lightweight yet generalizable detector for AI-generated image forgeries.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that reasoning texts produced by large language models about image authenticity can be distilled into a small visual model to improve its ability to spot fakes. This is done by using contrastive learning to make the model's image representations match the semantic and error-sensitive information in the texts. The resulting system is efficient for practical use while performing better on detecting sophisticated forgeries from modern generators. It combines this alignment with direct classification training to balance generalization and accuracy.

Core claim

ReAlign distills high-quality reasoning texts generated by a GRPO-optimized LLM into a lightweight AIGI detector via contrastive learning. It inherits the generalization ability and semantic sensitivity capability of reasoning textual representations while remaining efficient and lightweight for deployment, using a tailored joint optimization strategy that integrates contrastive loss for image-text alignment and classification loss for accurate forgery discrimination.

What carries the argument

Reasoning-aligned representation created by contrastive learning between image embeddings and LLM-generated reasoning texts about forgery.

If this is right

  • ReAlign outperforms state-of-the-art detectors in accuracy and generalization on benchmarks like AIGCDetectBenchmark.
  • It handles complex, high-fidelity forgeries from modern generative models effectively.
  • The method remains efficient and lightweight compared to full LLM-based approaches.
  • Joint optimization of alignment and classification losses improves both semantic understanding and forgery discrimination.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This could allow forgery detection to scale to new generative models without retraining large systems.
  • Similar alignment techniques might enhance other visual tasks with semantic reasoning from text.
  • Future work could explore using different types of reasoning texts or optimizing the LLM specifically for visual artifact description.

Load-bearing premise

The reasoning texts generated by the LLM carry generalization and semantic sensitivity that can be transferred effectively to the visual model through contrastive alignment.

What would settle it

Evaluating the detector on a dataset of forgeries where the LLM's reasoning texts do not highlight the actual visual inconsistencies would show no improvement over standard visual-only models.

Figures

Figures reproduced from arXiv: 2605.16080 by Jian Zhang, Qing Huang, Xiangyu Yu, Xuanyu Zhang, Zhipei Xu.

Figure 1
Figure 1. Figure 1: Evaluation Result on UltraSynth-10k and AIGCDe￾tectBenchmark [71]. Our ReAlign achieves SOTA performance. 1. Introduction With the rapid development of deep learning [24, 26, 42, 61] and generative technologies [51, 63, 64], AI-generated im￾ages (AIGIs) have become increasingly widespread, signifi￾cantly lowering the barrier to producing highly realistic im￾ages. However, their misuse poses security and et… view at source ↗
Figure 2
Figure 2. Figure 2: A study comparing LLM-based detectors and non-LLM-based detectors on different types of forgeries. We select AIDE as the non-LLM-based detection method, and AIGI-R1 as the LLM-based detection method. dient features, and inter-pixel relationships. Representative works include LGrad [43] used gradient maps generated by a classifier as features for GAN detection. UniFD [35] firstly utilized the vision-languag… view at source ↗
Figure 3
Figure 3. Figure 3: Visualization of the discriminative capability and generalization properties of reasoning text representations. LLM-based: In contrast, LLM-based AIGI detectors [27, 55, 73] encode images into visual tokens and fuse them with textual instructions before feeding them to the LLM. The model then generates both reasoning text within the <think> and </think> tags and the judgement answer within the <answer> and… view at source ↗
Figure 4
Figure 4. Figure 4: The pipeline of ReAlign. (a) The GRPO optimization pipeline of AIGI-R1. (b) Reasoning texts are collected from the trained AIGI-R1 and paired with the corresponding images to form a text-image pairs dataset. (c) Joint training of alignment and classification tasks for ReAlign based on the collected text-image dataset. (d) Using the trained ReAlign model for AIGI detection. question into the MLLM together. … view at source ↗
Figure 5
Figure 5. Figure 5: Sampled Examples of UltraSynth-10k [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
read the original abstract

The rise of AI-generated images (AIGIs) poses growing challenges for digital authenticity, prompting the need for efficient, generalizable image forgery detection systems. Existing methods, whether non-LLM-based or LLM-based, exhibit distinct advantages and limitations. While non-LLM-based models offer efficient low-level artifact detection, they often lack semantic understanding. Conversely, LLM-based methods provide strong semantic reasoning and explainability but are computationally intensive and less sensitive to subtle visual artifacts. Moreover, the true contribution of explanatory reasoning texts to forgery detection performance remains unclear. In this work, we investigate the intrinsic value and potential of LLM-generated reasoning texts, considering it a source of generalization and semantic-error sensitivity. Based on these findings, we propose ReAlign, a novel framework that distills high-quality reasoning texts generated by a GRPO-optimized LLM into a lightweight AIGI detector via contrastive learning. ReAlign effectively inherits the generalization ability and semantic sensitivity capability of reasoning textual representations, while remaining efficient and lightweight for deployment. Moreover, ReAlign adopts a tailored joint optimization strategy that integrates contrastive loss for image-text alignment and classification loss for accurate forgery discrimination. Experimental results on AIGCDetectBenchmark, AIGI-Holmes, and our newly constructed UltraSynth-10k demonstrate that ReAlign consistently outperforms existing state-of-the-art detectors in both accuracy and generalization, particularly when facing complex, high-fidelity forgeries from modern generative models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes ReAlign, a framework for generalizable AIGI forgery detection that distills reasoning texts generated by a GRPO-optimized LLM into a lightweight visual detector via contrastive learning. It combines image-text contrastive alignment with classification loss to transfer generalization and semantic sensitivity from the textual representations, while remaining efficient. Experiments claim consistent outperformance over prior detectors on AIGCDetectBenchmark, AIGI-Holmes, and the newly introduced UltraSynth-10k benchmark, particularly for high-fidelity forgeries.

Significance. If the results and ablations hold under full scrutiny, the work offers a practical bridge between low-level artifact detectors and semantically rich but heavy LLM approaches, potentially improving deployment in real-world authenticity verification. The construction of UltraSynth-10k and the explicit investigation of reasoning text value are constructive contributions to the field.

major comments (2)
  1. [Abstract and Methods (distillation pipeline)] The central hypothesis that LLM-generated reasoning texts supply transferable generalization and semantic-error sensitivity (Abstract) is load-bearing for the performance claims, yet the manuscript provides no ablations isolating reasoning text quality versus generic captions or no-text baselines; without these, the outperformance on the three benchmarks cannot be confidently attributed to the proposed distillation mechanism.
  2. [Experiments] Experimental results section: reported gains on AIGCDetectBenchmark, AIGI-Holmes, and UltraSynth-10k lack error bars, multiple random seeds, or statistical significance tests, undermining the generalization claim especially given the review's note on absent full protocols.
minor comments (2)
  1. [Method] Clarify the precise form of the joint contrastive-plus-classification objective and any weighting hyperparameters in the optimization strategy.
  2. [Conclusion] Add a dedicated limitations paragraph discussing potential failure modes when the GRPO-optimized LLM produces low-quality reasoning on novel forgery types.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and positive assessment of the work's potential significance. We address each major comment below with clarifications and commitments to revisions that strengthen the attribution of results to the proposed mechanism.

read point-by-point responses

  1. Referee: [Abstract and Methods (distillation pipeline)] The central hypothesis that LLM-generated reasoning texts supply transferable generalization and semantic-error sensitivity (Abstract) is load-bearing for the performance claims, yet the manuscript provides no ablations isolating reasoning text quality versus generic captions or no-text baselines; without these, the outperformance on the three benchmarks cannot be confidently attributed to the proposed distillation mechanism.

    Authors: We agree that isolating the contribution of reasoning texts is important for substantiating the central hypothesis. The manuscript does compare ReAlign against prior non-LLM and LLM-based detectors and includes a joint optimization analysis, but it lacks explicit ablations against generic captions or no-text baselines. In the revision we will add these experiments: (1) replacing reasoning texts with generic captions from a standard VLM such as BLIP, and (2) a no-text baseline that uses only the classification loss on image features. These additions will directly test whether the observed gains on AIGCDetectBenchmark, AIGI-Holmes, and UltraSynth-10k stem from the semantic-error sensitivity of the GRPO-optimized reasoning texts. revision: yes

  2. Referee: [Experiments] Experimental results section: reported gains on AIGCDetectBenchmark, AIGI-Holmes, and UltraSynth-10k lack error bars, multiple random seeds, or statistical significance tests, undermining the generalization claim especially given the review's note on absent full protocols.

    Authors: We acknowledge that the current single-run results limit confidence in the generalization claims. In the revised manuscript we will rerun all main experiments and ablations with at least three random seeds, report mean accuracy and standard deviation (error bars), and include statistical significance tests (paired t-tests or Wilcoxon signed-rank tests) against the strongest baselines. We will also expand the experimental protocols section with complete hyperparameter tables, training schedules, and data splits to address the noted absence of full protocols. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper's core pipeline relies on an external GRPO-optimized LLM to generate reasoning texts, which are then distilled into a lightweight visual detector through contrastive alignment plus joint classification loss. No derivation step, equation, or performance claim is shown to reduce by construction to a fitted parameter or self-defined quantity within the paper itself; the generalization and semantic-sensitivity benefits are presented as an empirical hypothesis tested on three external benchmarks. The framework is self-contained against those benchmarks and does not invoke load-bearing self-citations or uniqueness theorems that collapse the central result.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review based on abstract only; detailed free parameters, axioms, and invented entities cannot be extracted without the full manuscript.

axioms (1)
  • domain assumption LLM-generated reasoning texts serve as a source of generalization and semantic-error sensitivity for image forgery detection
    The paper states it investigates the intrinsic value of these texts as a basis for the distillation approach.

pith-pipeline@v0.9.0 · 5795 in / 1279 out tokens · 51931 ms · 2026-05-20T19:26:53.619540+00:00 · methodology

discussion (0)

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