Zoom-In to Sort AI-Generated Images Out

## Abstract

Abstract The rapid growth of AI-generated imagery has blurred the boundary between real and synthetic content, raising critical concerns for digital integrity. Vision-language models (VLMs) offer interpretability through explanations but often fail to detect subtle artifacts in high-quality synthetic images. We propose ZoomIn , a two-stage forensic framework that improves both accuracy and interpretability. Mimicking human visual inspection, ZoomIn first scans an image to locate suspicious regions and then performs a focused analysis on these zoomed-in areas to deliver a grounded verdict.
To support training, we introduce MagniFake , a dataset of 20,000 real and high-quality synthetic images annotated with bounding boxes and forensic explanations, generated through an automated VLM-based pipeline. Our approach achieves 96.39% accuracy with strong generalization across external datasets, and providing human-understandable explanations grounded in visual evidence.

## 1 Introduction

The rapid advancement of image generation models (Wang et al., 2025b; Li et al., 2025; Chadebec et al., 2025) has enabled the creation of AI-generated images with unprecedented photorealism, increasingly blurring the boundary between authentic and synthetic content. There is a critical need for platforms to deploy accurate and effective detection methods. However, the current landscape of detection methods is dominated by classification-based approaches. While often effective on specific datasets, these methods typically operate as “black- or gray-boxes”, offering little insight into their decision-making process. This lack of explainability is coupled with poor generalizability, as models trained to detect artifacts from one generative architecture often fail when confronted with novel, unseen ones.

The emergence of Vision-Language Models (VLMs) (Fang et al., 2025; Chen et al., 2024; Man et al., 2025; Wu et al., 2025c; b; Zhang et al., 2025) has opened a new frontier, offering a promising path towards semantic-level analysis and greater interpretability. Many recent approaches re-frame the detection task as a visual question answering (VQA) problem (Gao et al., 2025) or an image captioning task (Keita et al., 2025).
However, to reach high accuracy, these methods typically rely on external modules such as segmentation networks or additional classification heads (Zhou et al., 2025; Kang et al., 2025), which underutilize the intrinsic knowledge and common-sense reasoning already embedded in VLMs and reduce them to passive feature extractors.
More fundamentally, most approaches perform a single global pass over the image: the visual encoder compresses the scene into a limited set of tokens, attention is spread across the entire image, and fine-grained forensic cues (e.g., tiny text artifacts, stitching seams, periodic textures, specular edges) are weakened by downsampling and pooling (Park & Owens, 2024; Liu et al., 2023).
Without revisiting localized regions to test hypotheses, small but decisive artifacts are often overlooked, resulting in unreliable judgments on high-quality synthetic images.
As demonstrated in Figure [1](https://arxiv.org/html/2510.04225v1#S1.F1)(a), when the model cannot clearly perceive all details, it may rely on prior knowledge to “guess”, producing flawed reasoning and incorrect verdicts.

Figure: Figure 1: (a) Without revisiting specific details, VLMs may overlook critical cues and produce false reasoning with incorrect decisions. (b) Our two-stage ZoomIn pipeline. The VLM first performs a global scan to query region(s) of interest (Query 1), then analyzes the cropped regions for a detailed, final verdict with grounded explanations (Query 2, “Local Evidence Check”).
Refer to caption: x1.png

In this work, we propose a paradigm shift from passively classifying real and AI-generated images to actively reasoning with them. Rather than training a model for a single, holistic classification task, our approach emulates the process of a human expert, who first identifies suspicious regions and then “zooms in” for a closer look.
We bridged the reasoning and world knowledge of language models with the vision modality, building a system that inspects an image, hypothesizes about potentially synthetic regions, and then re-evaluates those specific regions to make a final, grounded decision.
To support this objective, we construct a dataset that provides cropped regions and grounded explanations for training. As illustrated in Figure [1](https://arxiv.org/html/2510.04225v1#S1.F1)(b), our model delivers reliable reasoning and correct decisions under the zoom-in paradigm.
Our experiments show that the proposed iterative, foveated approach enables a more robust and interpretable analysis.
Our major contributions are threefold:

- 1.
A Paradigm Shift to “Think with Images” in Forensics: We introduce a two-stage framework where a VLM first scans an image to hypothesize regions potentially indicating synthetic origin, and then performs a second, focused analysis on cropped regions to refine the verdict. This process grounds the final decision in explicit visual evidence.
- 2.
A Novel Data Annotation Pipeline: Leveraging state-of-the-art VLMs (e.g., GPT-4o) with detection-capable VLMs (e.g., Qwen-2.5-VL), we construct MagniFake, a dataset of 20,000 real and generated images annotated with bounding boxes and fine-grained forensic explanations.
- 3.
Robust Generalization with Interpretability: By directly linking the final verdict to visual evidence within identified bounding boxes, our method provides clear, human-intelligible explanations for its high accuracy robust to degradation and out-of-distribution data.

## 2 Related Works

#### Detection of AI-generated images.

Image forgery detection has evolved from traditional feature engineering to modern deep learning methods. Early detection methods rely on handcrafted features and statistical anomalies (Li & Zhou, 2019; Chen et al., 2021; Frank et al., 2020) or perform frequency-domain analysis for identifying GAN-specific artifacts (Jeong et al., 2022). The advent of deep learning marked a paradigm shift, with CNN-based detectors such as CNNSpot (Wang et al., 2020) demonstrating remarkable generalization capabilities across various GAN architectures when trained on ProGAN-generated (Gao et al., 2019) images. As generative models evolved beyond GANs to include diffusion models (Song et al., 2020; Ho et al., 2020; Le et al., 2025; Ye et al., 2025), detection strategies adapted accordingly (Corvi et al., 2023; Tan et al., 2023; Park & Owens, 2024; Ricker et al., 2024). Notably, DIRE (Wang et al., 2023) pioneered the use of reconstruction error metrics specifically tailored for diffusion-generated content. NPR (Tan et al., 2023) leveraged frozen CLIP encoders to maintain domain invariance.

Despite these advances, explainability and robust generalization remain major challenges. The emergence of VLMs offers a new frontier by enabling semantic-level analysis and natural language reasoning.
Many approaches re-formulate this classification problem to visual question answering (VQA) questions (Chang et al., 2023; Zhang et al., 2024b) or image captioning tasks (Keita et al., 2025). Several forensics datasets (Li et al., 2024; Zhang et al., 2024b; Gao et al., 2025) are curated using VLMs, creating training corpora that combine visual analysis with natural language reasoning.
Specifically,  Zhou et al. (2025) combines NPR (Tan et al., 2023) with LLM, achieving high detection accuracy with good interpretability.
In terms of localization, FakeShield (Xu et al., 2024) introduces the Segment Anything (Kirillov et al., 2023) module to acquire the tampered mask for the manipulated image.
LEGION (Kang et al., 2025) postfixes the vision encoder with an MLP to identify the authenticity of the input image.
Our work builds on this direction by introducing spatial grounding and iterative refinement, transforming VLMs from passive analyzers into active visual investigators that fully leverage their inherent common-sense reasoning.

#### Training and Fine-Tuning Reasoning-Capable VLMs.

Improving the reasoning abilities of VLMs is essential for tasks demanding sophisticated comprehension (Wu et al., 2025a; c; Yang et al., 2025a). Early approaches focused on transforming images into structured textual representations to facilitate language-based reasoning (Yang et al., 2025b). More recent studies have emphasized cultivating advanced cognitive skills, such as self-verification, self-correction, fostering “slow thinking” capabilities (Wang et al., 2025a), and regulating reasoning depth to mitigate issues like “overthinking” (Xiao et al., 2025). Additionally, efforts have concentrated on developing high-quality multi-modal Chain-of-Thought (CoT) datasets (Huang et al., 2025) to steer the reasoning process effectively.
DeepSeek-Math (Shao et al., 2024) provides a solid foundation and methodology for fine-tuning large language models.
For reward design, in addition to the outcome reward used in DeepSeek-Math, expert LLMs are also widely used as an online training reward provider (Lambert et al., 2024).
Researchers are also experimenting with using IoU (Pan et al., 2025) and BLEU (Chang et al., 2025) metrics as rewards.
Building on these advances, we train our VLM with spatially grounded supervision and iterative reasoning objectives, where cropped regions and fine-grained explanations guide the model to link decisions with explicit visual evidence, thereby enhancing both accuracy and interpretability.

Figure: Figure 2: The proposed data annotation pipeline. We ask the forensics expert VLM in Query 1 “Explanation Generation” to identify key reasons that make this image look real or AI-generated, followed by Query 2 “Spatial Grounding”, which uses the explanation to extract bounding boxes.
Refer to caption: x2.png

## 3 Methodology

### 3.1 Scan, Check and Verdict with the “Magnifying Glass”

Recently, thinking with image methods equip VLMs with visual tools to enable structured interaction with images, allowing step-wise reasoning and improved interpretability (Su et al., 2025c; Shen et al., 2025; Fan et al., 2025; Su et al., 2025a; Cheng et al., 2025).
Motivated by this idea, we design a detector that explicitly “zooms-in”, encouraging the model to take an additional step of focused reasoning.
Given an input image $I$, our goal is to build a VLM that outputs a final verdict $v\in\{\text{real},\text{generated}\}$ along with interpretable evidence, including bounding boxes and explanations, by mimicking the human forensic process of scanning, localizing suspicious regions, and then re-examining them under a magnifying glass.
To achieve this, we propose a two-stage inference pipeline inspired by how humans handle uncertainty: when uncertain about the whole image, they re-examine and focus more closely on regions that are most likely to be fake in order to increase confidence.
Figure [1](https://arxiv.org/html/2510.04225v1#S1.F1) shows the two-stage inference workflow. The setup is detailed below:

Query 1: Global Scan.
Given an input image $I$, we prompt a grounding-capable VLM to perform comprehensive visual analysis. The model generates:

- •
Initial verdict $v_{1}\in\{\text{real},\text{generated}\}$.
- •
Suspicious regions $B=\{b_{1},...,b_{n}\}$ where $b_{i}=(x_{1},y_{1},x_{2},y_{2})$ represents bounding box.
- •
Preliminary explanation $E_{1}$ articulating the reasoning.

Since VLMs may overlook such regions due to resolution or encoding limitations, magnified and focused inspection enables refined predictions and more reliable explanations.
This stage leverages the VLM’s ability to process global context while identifying locally anomalous regions, encouraging the model to “think with images” (Su et al., 2025b).
Figures [1](https://arxiv.org/html/2510.04225v1#S1.F1) and [2](https://arxiv.org/html/2510.04225v1#S2.F2) show examples for real and fake cases, respectively, illustrating the types of regions that warrant closer inspection.
We focus on two types: (i) regions inherently challenging for generative models, such as human faces or hands (Fig. [1](https://arxiv.org/html/2510.04225v1#S1.F1)), and fine-grained animal attributes like paws or poses (Fig. [2](https://arxiv.org/html/2510.04225v1#S2.F2)); and (ii) image-specific details that are difficult to reproduce, including logos on a referee’s shirt (Fig. [1](https://arxiv.org/html/2510.04225v1#S1.F1)) or small texts (Fig. [2](https://arxiv.org/html/2510.04225v1#S2.F2)).
By zooming into these regions, the model reduces global uncertainty, strengthens prediction accuracy, and provides more reliable explanations.

Query 2: Local Evidence Check.
For each identified region $b_{i}$, we first extract crops $C_{i}=\text{Crop}(I,b_{i})$, and then provide the VLM with both the original image $I$ and the crop collection $\{C_{i}\}_{i=1}^{n}$, enabling comparative analysis between global context and local details. This dual-input mechanism produces:

- •
Final verdict $v_{2}\in\{\text{real},\text{generated}\}$.
- •
Refined explanation $E_{2}$ grounded in specific visual evidence.

By providing these image crops $C_{i}$, we enrich the input with fine-grained visual tokens, allowing the model to correct earlier misjudgments through magnified analysis, much like a forensic expert using a “magnifying glass”. This two-stage verification significantly enhances the accuracy and interpretability of the prediction.
Enabling such reasoning requires training data with fine-grained annotations and supervision, which motivates the construction of our MagniFake dataset and the training of ZoomIn.

### 3.2 MagniFake Dataset Construction

Our two-stage pipeline requires localization information for both real and AI-generated images.
As discussed earlier, such information should highlight (i) regions inherently challenging for generative models and (ii) image-specific regions that are difficult to reproduce.
While the zoom-in process mimics the intuition of human forensic experts, VLMs do not naturally possess this capability. To enable this behavior and improve both grounding accuracy and detection performance, we design a targeted training strategy based on $(I,E,B,C)$ tuples, providing supervision for classification and grounding tasks within the pipeline.
To acquire grounding-aware training data, we create the MagniFake dataset by leveraging different VLM experts to generate textual explanations and bounding boxes.
Previous works (Gao et al., 2025; Ji et al., 2025) and our preliminary tests have confirmed that the OpenAI GPT-4o can produce detailed forensic explanations describing why an image is real or AI-generated.
Additionally, the Qwen-2.5-VL series of VLMs (Qwen Team, 2025) acan extract spatial regions from these explanations and output bounding boxes, making them well-suited for generating spatially grounded annotations.
We address the lack of spatially grounded forensic annotations through an automated pipeline as follows:

- 1.
Explanation Generation. For images with known labels, GPT-4o generates forensic explanations focusing on specific visual evidence.
The prompt used at this stage is designed to elicit detailed reasoning about the depicted objects, arrangement, perspective, and other relevant aspects of the given images.
- 2.
Spatial Grounding. Qwen-2.5-VL extracts bounding boxes from these explanations, creating $(I,E,B,C)$ tuples.

An overview of the pipeline is provided in Figure [2](https://arxiv.org/html/2510.04225v1#S2.F2), showing the prompts used in the automated annotation process.

#### Data Purification.

During the spatial grounding phase, the Qwen-2.5-VL model occasionally generates bounding boxes that cover more than 50% of the image, associating them with over-saturated global image imperfections.
In certain instances, these bounding boxes may revert to object detection when the primary object is clearly flawed but occupies a limited portion of the image.
To ensure only high-quality fake regions with logical captions are included in MagniFake, we further leverage a VLM to filter out redundant or excessively large regions from the dataset.
This filtration process evaluates whether the bounding box fully encapsulates the primary object (indicating a regression to object detection) or is disproportionately large, covering over 50% of the image, effectively removing them from the MagniFake dataset.

#### Image Distribution.

MagniFake consists of 10,000 real images and 10,000 AI-generated images. All of which are annotated with explanations and spatial grounding information, among which 99.5% of the images have at least one bounding box, with an average of 3.24 bounding boxes per image after filtering.
The real images are sourced equally from ImageNet (Deng et al., 2009) and COCO (Chen et al., 2015), and the AI-generated images are equally sourced from GPT-Image-1 (OpenAI, 2025a) and Gemini 2.5 Flash Image (Google, 2025).

### 3.3 Training Procedure of ZoomIn

With $(I,E,B,C)$ tuples from MagniFake, we fine-tune VLMs to serve as the ZoomIn detector.
Inspired by DeepSeek-Math (Shao et al., 2024), our fine-tuning approach employs a two-phase training paradigm that combines supervised fine-tuning (SFT) with reinforcement learning (RL) implemented through Group Relative Policy Optimization (GRPO).

#### Supervised Fine-tuning Phase.

The training begins with SFT to establish foundational capabilities and ensure stable model behavior. During this phase, all trainable parameters across the model’s vision encoder, projection layers, and language modeling components undergo optimization using supervised signals from the dataset.
This initial phase serves two critical purposes: (1) teaching the model to generate outputs that conform to our specified structured format, and (2) establishing baseline performance before applying reinforcement learning techniques.

#### Reinforcement Learning with Enhanced Rewards.

Following SFT, we implement RL through two GRPO stages.
Our reward design extends beyond traditional classification and localization metrics by incorporating linguistic quality assessment through BLEU scores (Chang et al., 2025).
We define reward functions tailored to two distinct query stages, each addressing specific aspects of the model’s performance.
In the first query, the model generates an initial hypothesis, focusing on format compliance and localization precision. The reward structure is defined as follows:

- •
Format Compliance: The reward for correct output formatting is given by:
$\mathcal{R}_{F}=1\text{ if output contains valid }\verb|<verdict>|\text{ tags}.$
(1)
- •
Localization Precision: Grounding accuracy is measured by IoU, rewarding precise spatial alignment. With $b_{i}$ denoting model-predicted boxes and $\hat{b}_{j}$ annotated boxes, we compute:
$\mathcal{R}_{\text{IoU}}=\frac{1}{|B|}\sum_{i}\max_{j}\operatorname{IoU}(b_{i},\hat{b}_{j}).$
(2)

This reward structure ensures the model prioritizes accurate spatial localization and adherence to the expected output format during the hypothesis generation phase.

In the second query, the model refines its hypothesis, emphasizing correct verdict prediction and high-quality explanation generation. The reward structure comprises:

- •
Classification Accuracy: The binary reward for correct verdict prediction is defined as:
$\mathcal{R}_{C}=\mathbbl{1}[v_{2}=y].$
(3)
- •
Explanation Quality: To encourage contextually appropriate explanations, we compute BLEU scores between generated explanations and reference texts:
$\mathcal{R}_{\text{BLEU}}=\operatorname{BLEU}_{2}(E^{\prime},E_{\text{ref}}).$
(4)
where $E^{\prime}$ is the explanation text from the model’s output, and $E_{\text{ref}}$ represents the reference explanation from our annotated dataset.

This reward structure drives the model to produce accurate verdicts and coherent, high-quality explanations during the refinement phase.
Section [4.3](https://arxiv.org/html/2510.04225v1#S4.SS3) shows the model performance when BLEU reward is ablated from the GRPO procedure, or when a correct verdict counts as a reward in the first query.

Figure: Figure 3: Examples from the test set of MagniFake, captions are summarized from the Query 2 response, generated by ZoomIn-32B.
Refer to caption: x3.png

## 4 Experiments

### 4.1 Setup

We use Qwen-2.5-VL (7B- and 32B-Instruct variants) (Qwen Team, 2025) trained on 8x NVIDIA A100 GPUs. The learning rates are set to $2\times 10^{-5}$ (SFT) and $10^{-5}$ (GRPO).
For ablation, we evaluate single-turn variants of Qwen-2.5-VL-Instruct: the untrained base model (Base), a version using only explanations (E-), explanations with grounding but without refinement (E+G-), and zoom-in variants trained with SFT only, without GRPO.
For baselines, we compare our models against traditional classification methods, including Community Forensics (Park & Owens, 2024), Antifake Prompt (Chang et al., 2023), DIRE (Wang et al., 2023), CNNSpot (Wang et al., 2020) and NPR (Tan et al., 2023).
For fair comparison, all baselines are retrained on MagniFake’s training split.
Due to their specific design, FakeShield (Xu et al., 2024) and LEGION (Kang et al., 2025) cannot be directly trained or fine-tuned on MagniFake because of incompatible dataset formats. Instead, we adopt the released pre-trained weights from FakeShield, and train LEGION on SynthScars (Kang et al., 2025) to reproduce their results.

### 4.2 Experimental Results

**Table 1: Detection accuracy and reasoning quality metrics on the MagniFake test set. E- stands for explanation-only, and E+G- stands for explanation and grounding-only.**
| Method | Acc. | I-Acc. | C-Acc. | C-Cases (%) | BLEU-1 | BLEU-2 | ROUGE-L | IoU |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| ZoomIn-7B | 0.942 | 0.866 | 0.915 | 9.2 | 0.314 | 0.209 | 0.291 | 0.316 |
| ZoomIn-32B | 0.972 | 0.873 | 0.956 | 10.9 | 0.346 | 0.211 | 0.327 | 0.359 |
| E-7B (one-turn) | 0.855 | - | - | - | 0.280 | 0.146 | 0.301 | - |
| E-32B (one-turn) | 0.869 | - | - | - | 0.294 | 0.153 | 0.315 | - |
| E+G-7B (one-turn) | 0.906 | - | - | - | 0.275 | 0.136 | 0.269 | 0.245 |
| E+G-32B (one-turn) | 0.914 | - | - | - | 0.282 | 0.149 | 0.295 | 0.254 |
| Base-7B | 0.553 | - | - | - | 0.110 | 0.032 | 0.073 | - |
| Base-32B | 0.587 | - | - | - | 0.102 | 0.043 | 0.079 | - |
| SFT-7B | 0.719 | 0.724 | 0.447 | 4.8 | 0.156 | 0.042 | 0.129 | 0.094 |
| SFT-32B | 0.715 | 0.706 | 0.584 | 5.3 | 0.159 | 0.076 | 0.130 | 0.105 |
| No BLEU Reward-32B | 0.929 | 0.868 | 0.871 | 8.2 | 0.187 | 0.095 | 0.153 | 0.296 |
| Verdict Only-32B (one-turn) | 0.897 | 0.897 | - | - | - | - | - | - |
| Dual Verdict Reward-32B | 0.944 | 0.948 | 0.473 | 7.4 | 0.276 | 0.164 | 0.252 | 0.260 |
| Random Cropping-32B | 0.842 | 0.873 | 0.421 | 19.6 | 0.105 | 0.043 | 0.109 | - |
| Largest 4 Bboxes-32B | 0.934 | 0.873 | 0.938 | 7.0 | 0.303 | 0.182 | 0.158 | - |
| Largest 3 Bboxes-32B | 0.924 | 0.873 | 0.919 | 6.1 | 0.299 | 0.183 | 0.147 | - |
| FakeShield (Xu et al., 2024) | 0.801 | - | - | - | 0.097 | 0.056 | 0.067 | 0.096 |
| LEGION (Kang et al., 2025) | 0.654 | - | - | - | 0.102 | 0.058 | 0.054 | 0.061 |

#### MagniFake Results.

In Table [1](https://arxiv.org/html/2510.04225v1#S4.T1), we report MagniFake results for all VLM-based methods, including all ZoomIn variants and the baselines LEGION (Kang et al., 2025) and FakeShield (Xu et al., 2024).
Accuracy is reported on the test split of the MagniFake dataset.
The results show that our method’s accuracy surpasses all existing detection methods, even with the smaller 7B variant.
Notably, compared to the original, untrained VLM baselines, our full pipeline with zoom-in reasoning yields an accuracy improvement of over 30%. Furthermore, when compared to single-turn variants, the zoom-in mechanism consistently contributes an additional 3.6% accuracy gain for the 32B and 5.8% for the 7B models, validating our core hypothesis that progressive visual reasoning enhances detection, reaffirming the paradigm shift to reason and think with images.

#### OoD Results.

In addition to our own dataset, we also evaluate our models on out-of-distribution (OoD) datasets, including GenImage (Zhu et al., 2023), MMFR-Dataset (Gao et al., 2025) and SynthScars (Kang et al., 2025).
For GenImage, we uniformly sample 10,000 images from the dataset and ensure that there is no image overlap with MagniFake as we also source some of the real images from ImageNet.
Note that LEGION is trained on SynthScars, so its results on this dataset are not considered OoD.
The scores are reported in Table [2](https://arxiv.org/html/2510.04225v1#S4.T2).
Our ZoomIn-32B model has an outstanding OoD performance, surpassing most baseline models and the 7B variant.

#### Reasoning Quality.

The generated reasoning is assessed for similarity to the MagniFake dataset using BLEU-1, BLEU-2, and ROUGE-L (Lin, 2004) metrics. The grounding capability is evaluated by the IoU, which represents the grounding accuracy of our models.
Table [1](https://arxiv.org/html/2510.04225v1#S4.T1) shows that the explanation quality improves for all model variants when introducing the zoom-in mechanism, consistently improving the responses’ alignment with the ground truth.

#### Discussions on refinement corrections.

Figure: Figure 4: The number of bounding boxes in Query 1 for ZoomIn-32B/7B models.
Refer to caption: x4.png

Figure [5](https://arxiv.org/html/2510.04225v1#S4.F5) demonstrates the correlation of accuracy and the number of zoom-in regions. We found that after training, the ZoomIn-32B and 7B models exhibit different tendencies in Query 1.
In Query 1, the ZoomIn-32B model outputs an average of 3.58 bounding boxes per image, while ZoomIn-7B gives 1.95.
The 32B model tends to ask for two to four close-ups per image, summing up to 83.15% among all input samples; meanwhile, the 7B model provides one or two bounding boxes for 86.34% of the cases.
To further investigate the model performance with regard to the number of bounding boxes, Figure [5](https://arxiv.org/html/2510.04225v1#S4.F5) shows that as the number of bounding boxes grows, the accuracy of ZoomIn-7B slightly lowers, while ZoomIn-32B has its accuracy spike at two bounding boxes, and performs consistently better than 7B on cases with more than two bounding boxes.
This phenomenon aligns with the fact that most training samples have an average of 3.24 bounding boxes.

**Table 2: Accuracy (%) of ZoomIn and other comparing methods on MagniFake, GenImage (Zhu et al., 2023), MMFR-Dataset (Gao et al., 2025) and SynthScars (Kang et al., 2025).**
| Datasets | GenImage | MMFR | SynthScars | MagniFake | Average |
| --- | --- | --- | --- | --- | --- |
| ZoomIn-32B | 0.915 | 0.893 | 0.852 | 0.972 | 0.908 |
| ZoomIn-7B | 0.878 | 0.892 | 0.826 | 0.942 | 0.885 |
| CommunityForensics (Park & Owens, 2024) | 0.854 | 0.838 | 0.760 | 0.876 | 0.832 |
| Antifake Prompt (Chang et al., 2023) | 0.916 | 0.862 | 0.819 | 0.927 | 0.881 |
| DIRE (Wang et al., 2023) | 0.872 | 0.889 | 0.815 | 0.922 | 0.874 |
| CNNSpot (Wang et al., 2020) | 0.853 | 0.846 | 0.807 | 0.855 | 0.840 |
| NPR (Tan et al., 2023) | 0.859 | 0.816 | 0.823 | 0.880 | 0.844 |
| LEGION (Kang et al., 2025) | 0.230 | 0.193 | 0.861 | 0.654 | 0.485 |
| FakeShield (Xu et al., 2024) | 0.864 | 0.710 | 0.765 | 0.801 | 0.785 |

### 4.3 Ablation Studies

We conducted several ablation studies on the 32B model variant to validate our design choices. Table [1](https://arxiv.org/html/2510.04225v1#S4.T1) shows the experimental results.
We report three metrics: the initial accuracy of the first turn (I-Acc.), the proportion of images forwarded to the second turn (C-Cases), and the corrected accuracy after refinement in the second turn (C-Acc.).

Contribution of the Zoom-In Mechanism.
For ZoomIn-7B and 32B models, 9.2% and 10.9% of the inputs will have a different verdict in Query 1 and Query 2 (i.e. $v_{1}\neq v_{2}$). Among these corrected cases, the accuracy is 91.5% and 95.6%, respectively, proving that models can refine their initial verdict with accurate grounding prior.

Impact of BLEU Rewards.
Removing linguistic quality rewards $R_{B}$ (“No BLEU Reward”) lowers the BLEU-$n$ and ROUGE-L metrics as expected. In addition to these reasoning quality metrics, we also note a 4.3% accuracy drop and a 6.3% IoU drop.
This demonstrates that encouraging the model to generate coherent explanations improves not just text quality, but also its underlying forensic reasoning capabilities.

Stage-Specific Rewards.
We experimented with applying the classification accuracy reward ($\mathcal{R}_{C}$) during Query 1, in addition to Query 2.
The result in row “Dual Verdict Reward” shows a slight performance degradation.
The model became overly cautious, often failing to propose suspicious regions unless it was already highly confident in its initial verdict, thus undermining the purpose of the second-stage refinement.
Specifically, with this reward setting, the zoom-in process will no longer benefit the performance, and the IoU was also lower than the original reward setup.
This confirms that focusing Stage 1 rewards on localization is the optimal strategy in exploiting the VLM’s intrinsic and learned capabilities in image forgery detection.
We also attempt to use the verdict as the only reward and remove the entire zoom-in process (“Verdict Only”). This results in slightly lower accuracy when compared to the E+G group and is more likely to overfit on the MagniFake dataset.

Random Cropping.
Our two-stage inference pipeline is dependent on both the original image and cropped images (“Random Cropping”).
Therefore, we further ablate the bounding box selection stage and use random cropping instead.
While the model remains as ZoomIn-32B, the bounding boxes given by the first query are no longer passed on to the second query.
Evaluation shows that when zooming in on random regions, the accuracy can drop to 84.2%, 13.0% lower than the initial verdict.
BLEU-$n$ and ROUGE-L metrics also drop to a level similar to the base model.
This indicates that without intelligent region proposal, the zoom-in mechanism is ineffective and the explanation quality is low.

Limiting Bounding Box Count.
Since Figure [5](https://arxiv.org/html/2510.04225v1#S4.F5) indicates that more bounding boxes may negatively impact model performance, we analyzed the impact of limiting the maximum number of proposed regions in Query 1 by selectively using the largest $n$ bounding boxes from the response.
As shown in Table [1](https://arxiv.org/html/2510.04225v1#S4.T1), using the largest three or four bounding boxes can cause reduced overall performance, with a 1.8% and 3.7% drop in correction accuracy, respectively.

### 4.4 Qualitative Analysis

Figure: Figure 6: An example of where ZoomIn-32B corrects its initial mistake upon zooming-in, finalizing with the correct conclusion that this image is real.
Refer to caption: x6.png

Figure [3](https://arxiv.org/html/2510.04225v1#S3.F3) illustrates representative examples of successful outcomes, while Figure [6](https://arxiv.org/html/2510.04225v1#S4.F6) presents a specific “corrected case”.
In this instance, ZoomIn-32B determines that the highly realistic and detailed rendering of window regions effectively refutes its initial assessment, thereby confirming that the image is likely a genuine photograph rather than an AI-generated picture.
Certain images with small text that are not clearly identifiable at first sight, minor anatomical issues (e.g., extra finger), or texture problems are highly likely to be corrected during the refinement query.

We observe the following common explanation patterns across successful predictions:

Lighting inconsistencies (12.4%): Unnatural reflections, impossible shadows.
Anatomical anomalies (8.6%): Distorted fingers, impossible joint positions.
Texture artifacts & blurry text (8.1%): Unnatural skin textures, clothing patterns.
Perspective errors (4.7%): Impossible spatial relationships, such as overlapping object parts.

Failure cases often involve highly realistic AI-generated images, especially human or animal faces, where visual artifacts are subtle or imperceptible, even under magnification. Conversely, some real images with too complex textures or patterns are occasionally misidentified as synthetic.

## 5 Conclusion

We present the MagniFake dataset and the zoom-in mechanism in image forensics, mimicking human visual examination patterns. Our method achieves both high accuracy (97.2%) and interpretability through spatially grounded explanations. The success of this two-stage approach suggests that complex problems may benefit from decomposition into interpretable sub-tasks rather than end-to-end black-box solutions.

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## Appendix A Dataset Appendix for MagniFake

The MagniFake dataset comprises 10,000 images (5,000 real and 5,000 AI-generated) annotated with forensic explanations and spatially grounded bounding boxes through an automated pipeline combining GPT-4o and Qwen-2.5-VL. This dataset addresses the critical need for grounding-aware training data in AI-generated image detection, particularly providing spatial localization capabilities alongside textual reasoning.

### A.1 Source of Images

To ensure comprehensive coverage across different image categories and generation techniques, we sourced images from established datasets and state-of-the-art generation models.

#### Real Images.

The authentic images are sourced from two widely used computer vision datasets: ImageNet (Deng et al., 2009) and COCO (Chen et al., 2015). These datasets provide diverse natural images spanning various object categories, ensuring broad coverage of real-world visual content. The selection from these datasets guarantees high-quality, authentic photographs that serve as reliable negative examples for training.

#### AI-Generated Images.

All synthetic images are created using the OpenAI GPT-Image-1 model (OpenAI, 2025b), representing state-of-the-art text-to-image generation capabilities. This choice ensures that our dataset captures contemporary AI generation artifacts and challenges, providing relevant training examples for current detection scenarios.

### A.2 Annotation Process

We developed a completely automated pipeline leveraging the complementary strengths of different VLMs to generate comprehensive annotations without requiring extensive human labeling.

Figure: Figure 7: A statistical analysis of keywords in MagniFake explanations.
Refer to caption: x7.png

Figure: Figure 8: A collection of images from MagniFake with rendered bounding boxes. The first row shows three AI-generated images with bounding boxes and corresponding explanations. The second row presents additional AI-generated samples, while the third row illustrates real images, all annotated with bounding boxes.
Refer to caption: x8.png

Figure: Figure 9: A visualization of the attention mechanisms of the VLM in Query 1.
Refer to caption: x9.png

#### Explanation Generation.

For images with known ground truth labels, GPT-4o generates detailed forensic explanations focusing on specific visual evidence that indicates whether an image is real or AI-generated. The prompts are designed to elicit detailed reasoning about depicted objects, spatial arrangements, perspective consistency, lighting patterns, and other forensic indicators. GPT-4o’s strong reasoning capabilities and knowledge of image forensics make it well-suited for generating high-quality explanatory text that identifies key visual cues.

#### Spatial Grounding.

Qwen-2.5-VL extracts bounding boxes from the GPT-4o generated explanations, creating spatially grounded annotations in the form of $(I,E,B,C)$ tuples, where $I$ represents the image, $E$ the explanation, $B$ the bounding boxes, and $C$ the cropped regions. The Qwen-2.5-VL model demonstrates strong capabilities in extracting spatial regions based on textual descriptions, making it suitable for converting explanatory text into precise spatial coordinates.

#### Quality Control and Filtering.

During the spatial grounding phase, we observed that Qwen-2.5-VL occasionally generates bounding boxes encompassing over 50% of the image area, often associated with global image characteristics such as over-saturation mentioned in the explanations. In some instances, the model reverts to object detection behavior when the primary flawed object occupies only a small portion of the image. To address these issues, we implement a filtering mechanism that leverages Qwen-2.5-VL to evaluate and remove bounding boxes that either fully encapsulate the primary object (indicating object detection regression) or cover an excessive portion of the image area. This filtration process ensures that the final annotations focus on specific forensic regions rather than global characteristics or entire objects.

#### Word Frequency Analysis.

Figure [7](https://arxiv.org/html/2510.04225v1#A1.F7) displays the most frequently occurring keywords in the annotations. Texture and object consistency emerge as the primary concerns, followed by unnatural lighting, shadows, and reflections, indicating that these are the features most commonly leveraged by the model for detecting synthetic content.

### A.3 More Samples from MagniFake

Figure [8](https://arxiv.org/html/2510.04225v1#A1.F8) presents additional examples from the MagniFake dataset, demonstrating the diversity of forensic indicators captured by our automated annotation pipeline.
The first row shows three AI-generated images with a brief summary of the explanation.
We can see that the explanations cover both fine-grained and general semantic reasoning of why this image should be considered real or AI-generated.
MagniFake features both real and AI-generated images. Four real image samples are provided at the bottom row.
This dataset covers a wide range of reasons, fostering explainability for fine-tuned models, and also demonstrating the complexity and variability inherent in synthetic imagery.

### A.4 Ethical Considerations

All AI-generated images in the dataset are created specifically for this research and do not depict real individuals.
The real images sourced from ImageNet and COCO are used in accordance with their respective licensing terms and ethical guidelines.

### A.5 Known Limitations

#### Automated Annotation Bias.

While our automated pipeline reduces human annotation costs, it may inherit biases from the underlying VLMs used for annotation. The quality of explanations and spatial grounding depends on the capabilities and training data of GPT-4o and Qwen-2.5-VL, potentially limiting the coverage of subtle or novel forensic indicators.

#### Language Limitation.

All explanations are generated in English, limiting the applicability of the dataset for multilingual forensic applications. Translation of the nuanced forensic explanations to other languages would require careful validation to maintain technical accuracy.

## Appendix B VLM Attention Visualization

In our multi-stage reasoning pipeline, we aim to enable the model to actively “look” for suspicious or diagnostically relevant regions within images, thereby facilitating deeper, more focused analysis in the second step.
A natural question arises: can the VLM truly identify image regions that are semantically aligned with the generated text and relevant to the real/fake detection task?

To verify whether our VLM, Qwen-2.5-VL-32B-Instruct, is indeed attending to specific, meaningful patches of the input image, rather than relying solely on global context or textual priors, we conducted a visualization study using gradient-based attention mapping on a representative sample from Query 1.
Specifically, we generated LLaVA-CAM (Zhang et al., 2024a) heatmaps to highlight the regions of the image that most strongly influence the model’s output predictions.
As shown in Figure [9](https://arxiv.org/html/2510.04225v1#A1.F9), there is a clear and compelling correspondence between the highlighted areas in the heatmap and the content of the model’s generated textual explanation, confirming that the model can localize and focus on relevant regions, which lays a solid foundation for the zoom-in process.
Moreover, we observe that attention often centers around keywords (e.g., “realistic”) in the textual explanation, reinforcing the connection between visual grounding and real/fake decision-critical semantics. This confirms that the model can meaningfully propose zoom-in regions that support reliable second-stage analysis.

## Appendix C More Experimental Details

Since real and AI-generated images are not of the same resolution or aspect ratio, we performed center-cropping and resizing to ensure all input images have a resolution of $512\times 512$ during training.

We use ms-swift to fine-tune VLMs.
During the GRPO stage, the number of generations is set to 2.
For ZoomIn-32B, the full training pipeline took 42.6 hours on 8x NVIDIA A100 GPUs. We found that at least 600 GB of VRAM is required to perform GRPO.
For ZoomIn-7B, the training took 35.3 hours on 4x NVIDIA A100 GPUs.
The training process is generally stable. A few loss spikes are observed during the first 1,000 steps of training, but the model quickly converges after that and recovers from the spike.

Figure: Figure 10: Number of samples grouped by the bounding boxes on OoD datasets.
Refer to caption: x10.png

#### Details of Baseline Methods

A range of methodologies has been proposed for detecting synthetic content, each grounded in distinct theoretical assumptions and detection paradigms.

CNNSpot (Wang et al., 2020) hypothesizes that CNN-based generative models leave consistent, detectable artifacts and achieve cross-generator generalization through data augmentation.
We trained CNNSpot from scratch on the training set of MagniFake. The training settings are the same as described in the original work.

Community Forensics (Park & Owens, 2024) adopts a data-centric approach, positing that detection performance scales with the diversity and quantity of training generators, and introduces a large-scale dataset comprising thousands of generators to train robust classifiers.

DIRE (Wang et al., 2023) takes a process-centric perspective, exploiting the asymmetric reconstruction behavior of diffusion models: real and generated images exhibit differing error patterns when reverse-denoised, forming a discriminative signal known as the DIRE map.

Antifake Prompt (Chang et al., 2023) leverages VLMs and reformulates detection as a visual question-answering task, employing parameter-efficient soft prompt tuning on a frozen VLM to enable generalization.

NPR (Tan et al., 2023) utilizes neighboring pixel relationships to identify AI-generated images with good accuracy and generalizability, as CNN-based generative methods exhibit patterns in neighboring pixels.

Collectively, these methods represent diverse strategies from artifact analysis to semantic reasoning, advancing the state of synthetic content detection.
During evaluation, all models are trained on the training set of MagniFake with the same setup as the original work.

## Appendix D Analysis of Bounding Boxes on OoD Datasets

We collected the responses from ZoomIn models when evaluating on OoD datasets, GenImage (Zhu et al., 2023), MMFR-Dataset (Gao et al., 2025) and SynthScars (Kang et al., 2025).
On these OoD datasets, Figure [11](https://arxiv.org/html/2510.04225v1#A3.F11) and [11](https://arxiv.org/html/2510.04225v1#A3.F11) display the relation of bounding boxes with regard to model performance, and the number of detected bounding boxes for each ZoomIn model variant (7B and 32B).
The trend of Figure [11](https://arxiv.org/html/2510.04225v1#A3.F11) highly resembles Figure 5 in the main paper, while Figure [11](https://arxiv.org/html/2510.04225v1#A3.F11) is slightly different than Figure 6, where 32B performs better than 7B for cases with more regions selected.
This proves that knowledge from the MagniFake dataset can be adapted beyond the dataset, and 32B generalizes better than 7B on OoD datasets.

## Appendix E Robustness Against Degradations

**Table 3: Performance on MagniFake with degradation, including JPEG compression artifacts, random cropping and image down-sampling.**
| Degradation | Metric | ZoomIn | FakeShield | LEGION | ComFor. | AfPr. | DIRE | CNNSpot | NPR |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| JPEG Compression<br>(80% Quality) | Acc. | 0.970 | 0.781 | 0.518 | 0.832 | 0.873 | 0.913 | 0.849 | 0.869 |
| IoU | 0.355 | 0.089 | 0.067 | - | - | - | - | - |  |
| JPEG Compression<br>(30% Quality) | Acc. | 0.964 | 0.768 | 0.505 | 0.791 | 0.852 | 0.896 | 0.837 | 0.835 |
| IoU | 0.347 | 0.086 | 0.066 | - | - | - | - | - |  |
| Random Cropping | Acc. | 0.965 | 0.756 | 0.513 | 0.835 | 0.877 | 0.909 | 0.848 | 0.866 |
| IoU | 0.306 | 0.061 | 0.063 | - | - | - | - | - |  |
| Downsampling<br>(0.5x) | Acc. | 0.969 | 0.759 | 0.514 | 0.890 | 0.886 | 0.912 | 0.851 | 0.874 |
| IoU | 0.346 | 0.075 | 0.070 | - | - | - | - | - |  |

We evaluate the robustness on MagniFake under four common degradations: JPEG compression at 80% and 30% quality, random cropping, and 0.5$\times$ downsampling (Table [3](https://arxiv.org/html/2510.04225v1#A5.T3)). All methods exhibit performance drops relative to clean images, with the extent varying across perturbations and models.

ZoomIn attains the highest accuracy in every setting and the best IoU among methods that produce localization, with modest declines across degradations (IoU: 0.355 at JPEG 80%, 0.347 at JPEG 30%, 0.346 under downsampling, and 0.306 with random cropping). Random cropping is most detrimental to localization quality, while heavy JPEG compression tends to reduce classification accuracy the most for several baselines. Among the baselines, DIRE consistently produces the best results, followed by AntifakePrompt and CommunityForensics, whereas all VLM-based methods show good robustness against JPEG compression.
Downsampling by 50% affects the performance across the models nonuniformly.

These results indicate that, despite relatively strong robustness, current detectors remain sensitive to common real-world degradations, while VLM-based methods have a lower rate, suggesting opportunities for improvement via degradation-aware training, stronger invariances, and reduced reliance on dataset-specific biases.

## Appendix F Computational Efficiency

**Table 4: Mean and standard deviation of inference time per image.**
| Method | Seconds / Image |
| --- | --- |
| ZoomIn-32B | 24.0$\pm$3.0 |
| E-32B (one-turn) | 10.9$\pm$2.2 |
| ZoomIn-7B | 8.94$\pm$1.21 |
| Base-7B (one-turn) | 4.38$\pm$0.71 |
| LEGION | 11.3$\pm$2.61 |
| FakeShield | 60.4$\pm$5.33 |
| NPR | 0.105$\pm$0.02 |
| AntifakePrompt | 0.182$\pm$0.05 |

Table [4](https://arxiv.org/html/2510.04225v1#A6.T4) lists the end-to-end inference time for our trained VLMs.
We use vllm (Kwon et al., 2023) to accelerate the inference process.
The deployment of ZoomIn-32B takes 4x NVIDIA A100-40G GPUs connected with PCI-E.
ZoomIn-7B, however, is deployed on one NVIDIA A100-40G GPU.
While our two-stage approach increases inference time from traditional classification methods, this overhead is justified by accuracy improvements and interpretability gains. For high-stakes forensic applications, the trade-off favors thoroughness over speed.

## Appendix G Limitations & Future Work

The generalization capability of our method has not yet been thoroughly evaluated. In future work, we aim to conduct more comprehensive assessments using a broader range of datasets and image sources to better understand the model’s detection accuracy across diverse scenarios.

While our current approach equips the model with a cropping tool to facilitate image-based reasoning, this represents only a preliminary step toward enabling true visual thinking. Despite its effectiveness, there remains significant potential for further exploration in this direction, particularly in developing more sophisticated interactive mechanisms that empower the model to dynamically analyze and reason over visual content.

## Appendix H The Use of Large Language Models

For this submission, we used large language models solely as writing assistants to improve grammar and fluency. They were not used for research ideation, methodological design, or generation of experimental results.
The research itself studies vision-language models as part of the proposed forensic framework, including their use in dataset annotation, which is integral to the contribution of this work.
All ideas, experiments, and analyses were conceived, designed, and verified by the authors, who take full responsibility for the content of the paper.

## Peer Reviews

confidence: 4
contribution: 2
initial_rating: 4
presentation: 2
questions: 1.Given that the BLEU reward only captures word similarity and not logical quality, did the authors consider using a more advanced reward model—to provide a more accurate training signal for the plausibility and logical soundness of the explanations?

2.How confident are the authors that the Query 1 proposal network can generalize to entirely novel classes of artifacts from generative architectures unseen during training?

3.How do the authors validate and mitigate the potential inherited biases introduced by the automated VLM-based dataset annotation pipeline?
soundness: 2
strengths: The paper introduces "ZoomIn," a two-stage framework that detects AI-generated images by first scanning for suspicious regions and then performing a focused analysis on those "zoomed-in" areas.

To train this model, the authors created "MagniFake," a new dataset of 20,000 real and synthetic images annotated with bounding boxes and forensic explanations.

This method achieves high accuracy (97.2%) and provides human-understandable explanations that are grounded in specific visual evidence
summary: This paper introduces ZoomIn, a two-stage forensic framework to improve the accuracy and interpretability of detecting AI-generated images. Mimicking human inspection , the model first performs a global scan to identify suspicious regions, then "zooms in" on these cropped areas for a detailed local analysis to make a final, grounded verdict. To train this, the authors developed MagniFake, a new dataset of 20,000 real and synthetic images annotated with bounding boxes and forensic explanations. The method achieves 97.2% accuracy and provides clear, human-understandable explanations grounded in visual evidence.
weaknesses: 1、The use of BLEU scores as a reward signal in the Reinforcement Learning (RL) phase is flawed, as it optimizes for superficial n-gram overlap rather than the logical correctness or causal soundness of the reasoning, risking that the model learns to mimic keywords rather than generate genuinely sound analysis.

2、The MagniFake dataset's AI-generated images are sourced from only two models, raising concerns that the detector may be overfitting to the specific artifact "signatures" of those generators.

3、 The analysis could be further strengthened by discussing and comparing against other relevant explainable MLLM-based detectors, for instance, FakeScope[1], So-Fake[2], and FakeVLM[3].  To provide a more comprehensive picture of the method's capabilities, it would be valuable to extend the evaluation to include additional testable benchmarks, such as LOKI[4] and Fakebench[5].

[1] Fakescope: Large multimodal expert model for transparent ai-generated image forensics

[2] So-Fake: Benchmarking and Explaining Social Media Image Forgery Detection.

[3] Loki: A comprehensive synthetic data detection benchmark using large multimodal models

[4] Spot the fake: Large multimodal model-based synthetic image detection with artifact explanation.

[5] FakeBench: Probing Explainable Fake Image Detection via Large Multimodal Models

---

confidence: 5
contribution: 2
initial_rating: 2
presentation: 1
questions: Please see the details above.
soundness: 2
strengths: 1. The paper is tackling a genuinely important, high-impact problem: forensics on high-quality modern synthetic imagery, and especially the need for interpretable decisions instead of opaque “fake/real” classifiers.

2. The two-stage pipeline from global hypothesis to zoom-in verification is clean and easy to understand.

3. The method links textual rationales to bounding boxes and then uses those localized crops to revise the verdict. That at least tries to connect why I think it’s fake to where in the pixels I saw it.
summary: The paper proposes ZoomIn, a two-stage VLM-based forensic pipeline for AI-generated image detection. Stage 1 predicts a provisional label (real vs AI-generated), highlights “suspicious” regions via bounding boxes, and explains why. Stage 2 crops those regions and asks the model again for a refined final verdict plus a more grounded explanation. To train this, the authors introduce MagniFake dataset produced by an automated pipeline: GPT-4o writes forensic justifications, and Qwen-2.5-VL turns those justifications into spatial boxes. The model is then fine-tuned with SFT and a GRPO-style RL stage using customized rewards.
weaknesses: 1. The paper repeatedly positions itself as a “paradigm shift” that turns VLMs from “passive analyzers into active visual investigators” and “encourages the model to think with images.” This is way too much of an oversell, since there have been a bunch of works adopting a similar pipeline, data construction, and training. The core mechanism, i.e., generate an initial answer, propose regions of interest, crop/zoom into them, and re-evaluate, is already very close to recent concepts in VLMs like grounded chain-of-thought or think-with-images. What ZoomIn adds is mainly: apply that template specifically to AI-generated image detection, and train it with a synthetic forensic dataset plus a custom RL reward. That is interesting, but it is nowhere near a conceptual paradigm shift for multimodal reasoning. There is no emerging recipe for a particular task.

2. A more serious blocker is that MagniFake is created entirely by GPT-4o and Qwen-2.5-VL. The paper calls these “annotations” and then treats them as supervisory ground truth. But GPT-4o is not a forensic expert; it is known to hallucinate plausible-sounding forensic cues. The paper itself even admits that large VLMs tend to “guess” when details are hard to perceive and can produce “false reasoning with incorrect decisions.” Besides, when generating the bounding box, he only cleanup step is a heuristic filter that removes bounding boxes that are too big or degenerate object detections. There is no mention of any human verification pass, inter-annotator agreement, or audit of factual correctness in those explanations and boxes. Trained on the curated dataset, the model is rewarded for matching its own synthetic teacher signals, then evaluated on how well it matches those same synthetic teacher signals. This is self-referential and does not demonstrate true interpretability.

3. In the main text, you say MagniFake has “10,000 real images and 10,000 AI-generated images,” sourced equally from ImageNet / COCO for real, and equally from GPT-Image-1 and Gemini 2.5 Flash Image for fake. But in Appendix A you describe MagniFake as “10,000 images (5,000 real and 5,000 AI-generated),” with AI-generated images created using only GPT-Image-1 (no Gemini), and you repeat that it is built from GPT-4o + Qwen-2.5-VL with automatic annotations.  What on earth is the setting? What on earth is the dataset used for training and testing, and what about the reported results?

4. Generator diversity is very narrow. Even if we take the main test as the claim, all synthetic images in MagniFake come from GPT-Image-1 and Gemini 2.5 Flash Image. That’s not broad enough to claim robust generalization across novel, unseen models. The employed datasets for OOD testing are still largely diffusion-style imagery.

5. Only accuracy is reported, no calibration or confusion breakdown. The model cannot output detection probability as confident scores. Besides, the employed performance measure is not convincing enough. Accuracy alone is insufficient in high-stakes forensic settings, where false positives and false negatives have very different consequences.

6. In AI-generated images, the telltale area normally has vague boundaries. How can you guarantee the bbxs actually reflect those areas?

7. “Reasoning quality” is measured by BLEU-1/2 and ROUGE-L comparing your generated explanations to the MagniFake reference explanations. This only shows stylistic and lexical similarity to a synthetic teacher, not factual grounding.

8. Several training/inference details critical for reproduction are missing. The authors fine-tuned the 7B and 32B Qwen with SFT over all parameters of the vision encoder, projection, and language model, then applied GRPO. But for a 32B model, full fine-tuning + RL on only “8× A100 GPUs” at the stated learning rates is, frankly speaking, unrealistic. What's your batch size? What about other parameter settings?

9. How do you decide to trigger Query 2 at inference time? Always? Only if confidence < threshold? This gating rule matters for runtime and deployment cost, but is not described concretely.

10. Sometimes you call the model “ZoomIn,” sometimes “ZoomIn-7B / 32B,” sometimes “ZoomIn detector,” sometimes “zoom-in mechanism.” It's better be consistent.

11. The evaluation measures, such as I-Acc., C-Acc., C-Cases, need explicit definitions in the main text.

12. You repeatedly say the model “mimics a human forensic expert.” If that is really the case, you have to demonstrate this with human expert studies or soften the narration.

---

confidence: 4
contribution: 2
initial_rating: 6
presentation: 3
questions: See above in weaknesses.
soundness: 3
strengths: 1. The paper is well-written and easy to follow, with clear motivations.
2. The experiment shows promising results and are extensive. It could potentially be a new baseline for this field.
3. The problem is framed clearly and the pipeline is intuitive.
summary: This paper presents ZoomIn, a framework for AI-generated image (AIGC) detection, and introduces MagniFake, a new dataset for training and evaluation. ZoomIn uses a two-stage inference pipeline: it first localizes suspicious artifact regions via bounding boxes, then zooms in to deliver a final verdict. To support training, the authors curate MagniFake (10k images) with region-level annotations produced by GPT-4o and Qwen-VL. By a two-stage training pipeline, the final models exhibit superior performance agains both traditional and llm-based methods, showing the effectiveness of the approach.
weaknesses: 1. Evaluation metrics: BLEU and ROUGE are both rule-based metrics. Are these appropriate metrics in the settings of open-ended reasoning? I believe incorporating llm as judges in this scenario is more suitable. Can you provide more insights on this?
2. Lack of evaluation baselines. Since the data is generated by Qwen-VL and GPT-4o, to show the improvement of ZoomIn over its distilled teacher, the paper should also consider including GPT-4o and Qwen (equiped with the same inference pipeline) for comparison.
3. The paper should also include LOKI[1] in the related work since this is also in the field of AIGC detection.

Reference:
[1] LOKI: A Comprehensive Synthetic Data Detection Benchmark using Large Multimodal Models

---

confidence: 3
contribution: 3
initial_rating: 4
presentation: 3
questions: 1. Could the authors provide an analysis of the conditions under which the Query 1 region proposals are likely to fail (e.g., with subtle or sparse artifacts)?
2. If Query 1 proposes no bounding boxes, does the model simply default to its initial (and potentially incorrect) global verdict, bypassing refinement?
soundness: 3
strengths: 1. Novel Two-Stage Framework: Proposes ZoomIn, a novel two-stage forensic framework that emulates human visual inspection (scan-then-focus), enhancing both accuracy and interpretability. Delivers robust, human-intelligible explanations by grounding its final verdict in explicit visual evidence found within focused, magnified regions.
2. Innovative Dataset: Introduces MagniFake, a large-scale dataset of 20,000 images annotated with bounding boxes and fine-grained forensic explanations, generated via an automated VLM pipeline.
3. High Accuracy & Generalization: Achieves high detection accuracy (97.2% on its test set) and demonstrates strong generalization to external, out-of-distribution (OoD) datasets.
summary: For the task of detecting AI-generated images, this paper introduces ZoomIn, a two-stage forensic framework designed to emulate the process of a human expert. To support this approach, the research also involved the construction of a new dataset, MagniFake, which was generated via an automated pipeline leveraging Vision-Language Models (VLMs).
weaknesses: 1. Critical Dependency on Query 1: The framework's success is critically dependent on the initial "Global Scan" (Query 1) stage, which acts as a performance bottleneck. If this stage fails to identify decisive artifacts, the entire "zoom-in" mechanism is rendered ineffective.
2. Insufficient Failure Mode Analysis: The ablation study on "Random Cropping" demonstrates the need for intelligent proposals but fails to analyze the robustness or failure modes (e.g., sub-optimal or incomplete proposals) of the proposal mechanism itself.
3. Risk of Annotation Bias: The fully automated dataset creation process (using GPT-4o and Qwen-2.5-VL) risks a "self-fulfilling prophecy," where the trained model learns the biases of the "teacher" VLMs rather than general, low-level forgery artifacts.