Redefining Generalization in Visual Domains: A Two-Axis Framework for Fake Image Detection with FusionDetect

## Abstract

Abstract The rapid development of generative models has made it increasingly crucial to develop detectors that can reliably detect synthetic images. Although most of the work has now focused on cross-generator generalization, we argue that this viewpoint is too limited. Detecting synthetic images involves another equally important challenge: generalization across visual domains. To bridge this gap, we present the OmniGen Benchmark . This comprehensive evaluation dataset incorporates 12 state-of-the-art generators, providing a more realistic way of evaluating detector performance under realistic conditions. In addition, we introduce a new method, FusionDetect , aimed at addressing both vectors of generalization. FusionDetect draws on the benefits of two frozen foundation models: CLIP & Dinov2. By deriving features from both complementary models, we develop a cohesive feature space that naturally adapts to changes in both the content and design of the generator. Our extensive experiments demonstrate that FusionDetect delivers not only a new state-of-the-art, which is 3.87% more accurate than its closest competitor and 6.13% more precise on average on established benchmarks, but also achieves a 4.48% increase in accuracy on OmniGen, along with exceptional robustness to common image perturbations. We introduce not only a top-performing detector, but also a new benchmark and framework for furthering universal AI image detection. The code and dataset are available here .

## 1 Introduction

Figure: Figure 1: FusionDetect performance on OmniGen and established benchmarks from previous works Zhu et al. (2024); Boychev & Cholakov (2024); Yan et al. (2025) compared to other detectors. The size of the bubble indicates the standard deviation of accuracy between all generators in the dataset (smaller is better).
Refer to caption: images/teaser.png

The field of artificial intelligence has entered an era of unprecedented creative capacity, primarily driven by the rapid maturation of text-to-image generative models Zhang et al. (2023). Recently, diffusion-based architectures such as Stable Diffusion Rombach et al. (2022), Midjourney MidJourney (2025), and Imagen Saharia et al. (2022) have achieved a level of photorealism and artistic flexibility that was once the domain of science fiction. These models have democratized content creation, empowering users to generate complex, high-fidelity images from simple textual descriptions. This technological leap has unlocked vast potential in domains ranging from digital art Saharia et al. (2022); Nichol et al. (2021) and entertainment Stark (2024) to product design Wang et al. (2025) and scientific visualization Thampanichwat et al. (2025). However, this accessibility is a double-edged sword. The same tools that foster creativity can be wielded for malicious purposes, including the generation of convincing disinformation, the creation of synthetic media to erode public trust, and the violation of copyright and personal identity Xu et al. (2023); Ren et al. (2024); Samrouth et al. (2024). Consequently, the development of robust, reliable, and universal methods for detecting AI-generated images has become a critical imperative for ensuring the integrity of our digital ecosystem Mahara & Rishe (2025).

The academic pursuit of AI-generated image detection has evolved significantly, yet it faces persistent challenges that limit its real-world applicability. Early research Wang et al. (2020); Zhang et al. (2019); Qian et al. (2020) focused heavily on identifying artifacts from Generative Adversarial Networks (GANs) Goodfellow et al. (2014). Although fundamental, this focus is increasingly obsolete due to the overwhelming dominance of diffusion models Ho et al. (2020); Song et al. (2021). These models Ho et al. (2020); Dhariwal & Nichol (2021); Song et al. (2021); Rombach et al. (2022) are the backbone of nearly all state-of-the-art (SOTA) commercial, open-source, and community-driven projects. A modern, practical detector must therefore be also engineered for the unique and subtle characteristics of this new paradigm.

More critically, we argue that the community’s understanding of generalization is dangerously incomplete as they focus on only one aspect of it. This typically involves training a detector on images from a single generator and evaluating its ability to identify images from a variety of other generators Ojha et al. (2023); Wang et al. (2023); Yan et al. (2025). To rectify this, we formalize the problem as a two-axis generalization challenge: a truly universal detector must demonstrate robustness not only on the well-studied cross-generator axis (handling unseen generators) but also on the often-neglected cross-semantic axis (handling unseen visual domains). As we will show, prior works often fails on the second axis, rendering it unreliable for real-world Yan et al. (2025). This semantic gap is not merely theoretical. As our t-SNE Maaten & Hinton (2008) projection in Figure [2](https://arxiv.org/html/2510.05740v1#S1.F2) visualizes, popular datasets like GenImage Zhu et al. (2024), ImagiNet Boychev & Cholakov (2024), and the challenging Chameleon Yan et al. (2025) form distinct, non-overlapping clusters in our proposed FusionDetect embedding space. As shown, there is no to little overlap between each dataset cluster. This demonstrates that a model trained exclusively on the feature distribution of one dataset will fail to recognize the patterns of another, regardless of the generator used.

Figure: Figure 2: T-SNE Maaten & Hinton (2008) projection of GenImage Zhu et al. (2024), ImagiNet Boychev & Cholakov (2024), and Chameleon Yan et al. (2025) dataset.
Refer to caption: images/dataset-emb.png

To solve this two-axis challenge, we propose FusionDetect, a powerful fusion model engineered for universal AI image detection. We hypothesize that a truly robust and generalizable representation can only be created by combining the complementary strengths of large-scale, foundational models with orthogonal training objectives. Instead of hunting for a single, elusive universal artifact, FusionDetect fuses deep features from two distinct and powerful vision encoders: CLIP Radford et al. (2021) for its unparalleled semantic breadth and DINOv2 Oquab et al. (2023) for its profound understanding of fine-grained structure and texture.

To facilitate a more rigorous and realistic evaluation of detector performance, we introduce the OmniGen Benchmark, a new, open-source test set designed to capture the modern generative landscape. The OmniGen benchmark directly addresses the weaknesses of prior benchmarks by including images from 12 SOTA generators, such as closed-source models, the latest open-source architectures, and popular community fine-tunes. By curating this benchmark with high semantic variety, we provide a robust framework to validate a model’s performance along both axes of generalization, ensuring that our evaluations reflect a detector’s true capabilities in real-world scenarios. [1](https://arxiv.org/html/2510.05740v1#S1.F1)

In summary, the primary contributions of this paper are fourfold:

- 1.
We formalize the ”two-axis generalization” problem in AI image detection, highlighting the critical need for models to generalize across both unseen generators and semantic domains.
- 2.
We introduce FusionDetect as a strong proof-of-concept for this framework. It demonstrates that fusion of complementary foundational features can decisively outperform more complex architectures when evaluated under the two-axis setting.
- 3.
We release the OmniGen Benchmark, the first test set explicitly designed to test two-axis generalization, featuring 12 diverse SOTA generators and high semantic variance.
- 4.
We demonstrate through extensive experiments that FusionDetect establishes a new SOTA, achieving superior generalization and robustness to common image perturbations compared to existing methods.

## 2 Related Work

The field of AI-generated image detection is in a constant race against generative technology. To provide context for our work, we’ll first review the evolution of generative models, from older GANs Goodfellow et al. (2014); Karras (2017); Brock et al. (2018) to modern diffusion models Dhariwal & Nichol (2021); Rombach et al. (2022); Nichol et al. (2021). We’ll then look at the detection methods, highlighting how each has responded to the shifting capabilities of generative architectures. Our review shows that existing detection methods have consistently lagged behind generative advancements, a critical gap that our work aims to close by addressing the ”two-axis generalization” problem.

### 2.1 Image Generation

The field of synthetic image generation has been reshaped over the last decade. It has transitioned from early breakthroughs with GANs Karras (2017); Karras et al. (2019); Brock et al. (2018) to the current dominance of Diffusion Models (DMs). The advent of Denoising Diffusion Probabilistic Models (DDPMs) Ho et al. (2020) marked a significant paradigm shift. Diffusion Models (DMs) and their subsequent variants have now surpassed GANs in terms of image quality, diversity, and text-to-image coherence Dhariwal & Nichol (2021). The initial wave of practical diffusion models was led by the Latent Diffusion Model (LDM) architecture Rombach et al. (2022), which underpins the widely popular Stable Diffusion series. These models made high-fidelity generation accessible to the public and became a foundational tool for both research and creative applications.

The pace of innovation has since accelerated, leading to a new generation of even more sophisticated architectures. Architectural upgrades in models like Stable Diffusion XL (SDXL) Podell et al. (2023), such as a larger UNet Ronneberger et al. (2015) and dual text-encoders, have led to significant improvements in image quality and prompt fidelity. The field continues to evolve rapidly with new open-source models like FLUX Labs et al. (2025), SD3.5 Esser et al. (2024), HiDream Cai et al. (2025), CogView4 Zheng et al. (2024), Kandinsky3 Vladimir et al. (2024), PixArt-$\delta$, alongside closed-source counterparts like Google’s Imagen Saharia et al. (2022) and Midjourney MidJourney (2025) and also community finetuned models such as Juggernaut RunDiffusion (2025) and Dreamshaper Lykon (2025). This model shift from GANs to diffusions generates a new class of synthetic images with distinct statistical fingerprints that challenge existing detection methodologies, a primary focus of this work.

### 2.2 Image Detection

Detection methodologies can be broadly categorized into two main paradigms: those that seek to identify specific, inherent artifacts of the generation process, and those that leverage the general-purpose feature representations of large pretrained foundational models.

#### Artifact-Based Detection

This paradigm is founded on the principle that the synthetic generation process, regardless of its sophistication, leaves behind subtle, machine-detectable traces or ”fingerprints” Sinitsa & Fried (2024). Researchers have pursued these artifacts across various domains. A significant body of work targets universal image properties, analyzing inconsistencies in the frequency domain (Frank et al. (2020); Tan et al. (2024); Sinitsa & Fried (2024); Qian et al. (2020)), exploring local texture and patch-level correlations (Zhong et al. (2023); Tan et al. (2023); Chen et al. (2024b)), or extracting unique residual noise patterns left by the generation process (Zhang & Xu (2023); Liu et al. (2022)). More recently, a modern class of artifact-based detectors leverages the internal mechanics of the diffusion process itself as a forensic tool. This approach is broadly divided into error-based and non-error-based methods. Error-based detectors operate on the principle that diffusion models reconstruct their own outputs with lower error than real images, using this discrepancy in pixel space (Wang et al. (2023); Ma et al. (2023)), in latent space (Ricker et al. (2024)), or as a guiding feature (Luo et al. (2024)). In contrast, non-error-based methods use the diffusion pipeline in other ways, such as to generate hard negative training samples (Chen et al. (2024a)), to extract internal representations like noise maps as features (Cazenavette et al. (2024)), or to distill a slow, error-based model into a faster one (Lim et al. (2024)). A detailed overview of these detection paradigms is provided in Appendix [7.3](https://arxiv.org/html/2510.05740v1#S7.SS3).

Despite their successes, our experiments indicate that artifact-based detection methods face significant limitations. First, their performance is often brittle, demonstrating poor cross-generator generalization. As generative models evolve, the specific artifacts these methods rely on change, making the detectors quickly outdated. Second, they are highly sensitive to common image perturbations, like compression, which can easily destroy the subtle fingerprints they detect.

#### Pretrained Feature-Based Detection

A more recent and increasingly dominant paradigm moves away from specialized artifact detection and instead leverages the rich feature spaces of large-scale, pretrained foundational models Yan et al. (2025); Ojha et al. (2023); Keita et al. (2025). The core idea is that these models, having been trained on web-scale data, have learned robust and generalizable representations of the visual world. A pioneering work in this area is UniFD Ojha et al. (2023), which demonstrated that a simple linear classifier trained on CLIP Radford et al. (2021) features can achieve impressive generalization across unseen generators. This highlighted the power of semantic features for the detection task. Other works have explored this vision-language connection further; for example, Bi-LoRa Keita et al. (2025) reframes the detection problem as a visual question-answering or captioning task. Methods like LASTED Wu et al. (2023) also leverage language-guided contrastive learning. AIDE Yan et al. (2025), proposed a hybrid model that combined semantic features from a pretrained CLIP model with specialized, hand-crafted modules (DCT Ahmed et al. (2006) and SRM Fridrich & Kodovsky (2012) filters) to capture low-level texture statistics.

The success of sophisticated hybrid approaches like AIDE Yan et al. (2025), raises a critical question: is it necessary to design hand-crafted modules for low-level features, or can a more effective and less complex solution be found by fusing the features of two distinct, general-purpose foundational models? To answer this question we proposed FusionDetect that utilized feature fusion of foundation models and experiment on the impact of such approach.

## 3 Methodology

This section details FusionDetect, a model explicitly designed to solve the two-axis generalization problem. We formally define this as training a detector $D$ on a distribution of generators $G_{train}$ and semantic domains $S_{train}$ that must generalize to a test set drawn from $G_{test}$ and $S_{test}$, where $G_{train}\cap G_{test}=\emptyset$ and $S_{train}\cap S_{test}=\emptyset$. FusionDetect addresses this by creating a hybrid feature space engineered to be a strong baseline detector invariant to shifts in both $G$ and $S$.

#### FusionDetect

The feature extraction backbone of FusionDetect consists of two distinct, powerful vision encoders. A key design choice is that both of these pretrained backbones remain frozen during training. This helps with computation efficiency and faster training time as well as preventing the model from overfitting to the training data and preserving the highly generalizable, world-knowledge features learned by these models during their original large-scale pretraining.

The two branches of our feature extractor are as follows:

- 1.
Semantic Feature Encoder (CLIP): We employ a CLIP vision encoder to capture high-level semantic, contextual, and object-level information. Its rich understanding, derived from large-scale image-text pretraining, is crucial for achieving cross-semantic generalization. Given an input image $I$, the CLIP encoder $E_{CLIP}$ produces a semantic feature vector.
- 2.
Structural Feature Encoder (DINOv2): We use a DINOv2 vision transformer to capture fine-grained structural and textural details. As a self-supervised model, it is highly sensitive to the low-level patterns and artifacts that betray a synthetic origin, which is vital for achieving cross-generator generalization. The DINOv2 encoder $E_{DINO}$ processes the same input image $I$ to produce a structural feature vector.

These two feature vectors are then fused via concatenation to form a comprehensive hybrid feature vector, $z_{f}$, assuming $d_{clip}$ and $d_{dino}$ are the image encoders output dimensions:

$$ $z_{f}=[E_{CLIP}(I)\in\mathbb{R}^{d_{clip}}\mathbin{\|}E_{DINO}(I)\in\mathbb{R}^{d_{dino}}]\in\mathbb{R}^{d_{clip}+d_{dino}}$ (1) $$

$z_{f}$, is then processed by the only trainable component of our model: a lightweight Multi-Layer Perceptron (MLP) classifier head, denoted by the function $f_{\theta}$ with parameters $\theta$. The model is trained end-to-end to minimize the binary cross-entropy (BCE) loss $L(\theta)$ over a batch of $N$ images, defined as:

$$ $L(\theta)=-\frac{1}{N}\sum_{i=1}^{N}[y_{i}\log(p_{i})+(1-y_{i})\log(1-p_{i})]$ (2) $$

where $y_{i}\in\{0,1\}$ is the ground-truth label and $p_{i}=\sigma(f_{\theta}(z_{f,i}))$ is the predicted probability from the sigmoid function $\sigma$.

## 4 Experiments

To empirically validate the effectiveness of our proposed FusionDetect model, we conduct a series of comprehensive experiments. Our evaluation is designed to rigorously test performance along the two-axis generalization problem, assess robustness to real-world image perturbations, and dissect the model’s architecture to understand the contribution of its core components.

### 4.1 Experimental Setup

Implementation Details: The FusionDetect model was trained for an efficient 10 epochs using an AdamW optimizer on a single NVIDIA RTX 3090 GPU. The final architecture consists of frozen CLIP-ViT-L14 and DINOv2-L14 backbones and a 4-layer MLP classifier head. To enhance robustness, we applied random JPEG compression and Gaussian blur to 10% of the images during training.

Baselines for Comparison: We compare FusionDetect against a comprehensive suite of recent detectors which their code and pretrained weights were publicly accessible: DIF Sinitsa & Fried (2024), UNIFD Ojha et al. (2023), DNF Zhang & Xu (2023), LASTED Wu et al. (2023), BiLORA Keita et al. (2025), AIDE Yan et al. (2025), SSP, and NPR. To ensure a thorough and fair evaluation, these models are tested in two settings where applicable: using their original, off-the-shelf pretrained weights, and after being retrained from scratch on our custom dataset.

Training Dataset: To directly address the semantic generalization gap, we curated a custom, balanced training set of 60,000 images (30k real, 30k fake). This dataset was constructed to maximize categorical and stylistic diversity by combining three distinct sources: samples derived from the large-scale ImagiNet Boychev & Cholakov (2024) and GenImage Zhu et al. (2024) benchmarks, and a challenging set of images generated using prompts derived from the hyper-realistic Chameleon dataset Yan et al. (2025). To follow the training scheme of previous work for cross-generator generalization, only images generated by SD1.4 and SD2.1 Rombach et al. (2022) were used in the train dataset and tested on others generators and datasets.

Evaluation Metrics: Similar to previous work, we report performance using Accuracy (Acc) and Average Precision (AP) Yan et al. (2025); Wu et al. (2023); Tan et al. (2024); Ojha et al. (2023). To specifically measure generalization, we compute the both the Mean and Standard Deviation (STD) of these metrics across diverse benchmarks. A lower STD is a critical indicator of a robust detector, as it signifies consistent performance across different semantic domains. Accuracy of each class (real/fake) has also been reported in Appendix [7.4](https://arxiv.org/html/2510.05740v1#S7.SS4).

### 4.2 Comparative Analysis on Established Benchmarks

To validate the generalization capabilities of FusionDetect and provide a comparison point to prior work, we evaluated it on a collection of diverse and established test sets. The test set contains 8000, 10000, and 2595 images from GenImage Zhu et al. (2024), ImagiNet Boychev & Cholakov (2024) and Chameleon Yan et al. (2025) datasets respectively, each containing equal number of real and synthetic images. To ensure a fair and comprehensive evaluation, the test set is composed of an equal number of images sampled from every available generator within each source dataset. A detailed overview can be found in Appendix [7.2](https://arxiv.org/html/2510.05740v1#S7.SS2).

As shown in Table [1](https://arxiv.org/html/2510.05740v1#S4.T1), FusionDetect achieves the best overall performance, attaining the highest mean accuracy and average precision, and crucially, the lowest standard deviation. It surpasses the closest competitor by $3.87\%$ in accuracy, $6.13\%$ in average precision. An important note is that although other detectors perform better on GenImage Zhu et al. (2024), but on ImagiNet Boychev & Cholakov (2024) and the difficult chameleon Yan et al. (2025) dataset our detector outperforms others by almost $10\%$ on average which indicates that previous models were specifically designed to perform well on the GenImage benchmark since it was seen as the standard benchmark for synthetic image detection; and not as a universal detector. Acheiving low STD and high mean accuracy on three different datasets indicates the domain generalization capabilities of FusionDetect.

**Table 1: Performance comparison on established benchmarks. Models marked with * were evaluated using their official pretrained weights. All other baselines were retrained on our custom training set. Results are in the format: Acc / AP (%). Best overall performance is bold, second best is underlined.**
| Detector | GenImage | ImagiNet | Chameleon | STD | Mean |
| --- | --- | --- | --- | --- | --- |
| BiLoRA* | 61.20 / 61.13 | 56.56 / 53.61 | 50.54 / 42.21 | 5.34 / 9.53 | 56.10 / 52.32 |
| DIF* | 91.80 / 65.20 | 50.20 / 99.60 | 53.30 / 87.10 | 23.17 / 17.41 | 65.10 / 81.97 |
| UNIFD* | 70.49 / 88.93 | 76.16 / 85.67 | 50.76 / 54.49 | 13.33 / 19.01 | 65.80 / 76.36 |
| NPR* | 75.70 / 81.70 | 74.60 / 74.50 | 54.20 / 37.89 | 12.11 / 23.49 | 68.17 / 64.70 |
| DNF | 77.90 / 99.71 | 74.42 / 91.79 | 57.37 / 35.30 | 10.99 / 35.12 | 69.90 / 75.60 |
| UNIFD | 73.42 / 79.02 | 71.74 / 79.01 | 64.56 / 64.58 | 4.71 / 8.33 | 69.91 / 74.20 |
| LASTED | 73.14 / 61.20 | 74.10 / 59.98 | 69.31 / 62.33 | 2.53 / 1.18 | 72.18 / 61.17 |
| AIDE | 88.51 / 97.09 | 69.23 / 85.46 | 59.63 / 67.49 | 14.71 / 14.91 | 72.46 / 83.35 |
| LASTED* | 93.61 / 76.74 | 62.74 / 59.88 | 61.42 / 55.27 | 18.22 / 11.30 | 72.59 / 63.96 |
| NPR | 86.50 / 94.10 | 72.70 / 87.60 | 59.10 / 61.50 | 13.70 / 17.25 | 72.77 / 81.07 |
| AIDE* | 87.11 / 98.06 | 72.65 / 83.73 | 61.82 / 63.08 | 12.69 / 17.58 | 73.86 / 81.62 |
| SSP* | 93.59 / 99.10 | 75.61 / 82.81 | 58.64 / 64.24 | 17.48 / 17.44 | 75.95 / 82.05 |
| SSP | 93.34 / 96.86 | 77.99 / 80.19 | 59.64 / 66.77 | 16.87 / 15.07 | 76.99 / 81.27 |
| \rowcolorblue!10<br>FusionDetect | 83.03 / 91.28 | 83.23 / 90.91 | 76.32 / 80.02 | 3.93 / 6.41 | 80.86 / 87.40 |

### 4.3 The OmniGen Benchmark

A core contribution of our work is the creation of a new, open-source benchmark designed to reflect the practical challenges of AI image detection. Our motivation was to address the shortcomings of existing benchmarks, which often lack semantic diversity and lag behind the rapid pace of generator development. The OmniGen benchmark was designed to be more practical and challenging by focusing on the latest SOTA generators, including both closed-source APIs and popular fine-tuned community models.

Generator Selection: The benchmark contains 11,550 fake images from a curated list of 12 relevant and powerful generative models, categorized as follows:

- •
Closed-Source: GPT-4o Hurst et al. (2024), Imagen 4 Saharia et al. (2022), Imagen 4 ultra Saharia et al. (2022), MidJourney v7 MidJourney (2025).
- •
Open-Source: FLUX 1 Labs et al. (2025), Kandinsky 3 Vladimir et al. (2024), PixArt-$\delta$ Chen et al. (2024c), SD3.5-medium Esser et al. (2024), HiDream-I1 Cai et al. (2025), CogView4-6B Zheng et al. (2024).
- •
Community Fine-tuned: Juggernaut RunDiffusion (2025), Dreamshaper Lykon (2025).

Benchmark Curation: To ensure high semantic diversity and prevent model overfitting to specific concepts, the synthetic images were generated using a structured, randomized prompt template:

”A richly detailed, high-resolution and photorealistic image depicting: {subject} during the {time}. The scene includes {setting}, {visual}, and lifelike rendering. The image style resembles {style}. Use {light}.”

Each bracketed variable was populated from a large pool of options (e.g., over 400 different subjects), resulting in highly unique prompts for each image. For each of the 12 generators, we generated 1000 images with $1024\times 1024$ resolution which were evaluated against a set of 1000 real images from Unsplash uns. A detailed overview and examples generated can be found in Appendix [7.1](https://arxiv.org/html/2510.05740v1#S7.SS1).

Our secondary evaluation is conducted on the new OmniGen benchmark, which is designed to test detectors against the modern, real-world generative landscape. The results shown in Table [2](https://arxiv.org/html/2510.05740v1#S4.T2) demonstrate the superior performance of FusionDetect, specifically in accuracy where we see $+4.48\%$ increase in performance. Among the detectors, FusionDetect is the top performer, achieving the highest mean accuracy of $97.38\%$ and a remarkably low standard deviation of $3.26$. This indicates its consistent ability to generalize across a wide variety of SOTA generators, from closed-source APIs to open-source and community. AP and class accuracies are reported in [7.4](https://arxiv.org/html/2510.05740v1#S7.SS4).

**Table 2: Comparison of detectors on our proposed OmniGen test set on Accuracy (%). Models marked with * were evaluated using their official pretrained weights. Best results are in bold and second best is underlined.**
| Generator | DIF* | UNIFD* | UNIFD | DNF | LASTED* | BILORA* | AIDE* | AIDE | SSP* | NPR | LASTED | SSP | \columncolorblue!10<br>Ours |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| GPT-4o | 52.8 | 71.5 | 73.9 | 82.9 | 82.1 | 79.6 | 87.6 | 82.9 | 97.2 | 99.4 | 85.7 | 98.3 | \columncolorblue!1097.3 |
| Imagen 4 | 50.3 | 49.6 | 62.0 | 72.8 | 73.7 | 79.4 | 49.2 | 51.2 | 56.8 | 88.6 | 96.7 | 74.9 | \columncolorblue!1097.5 |
| Imagen 4 Ultra | 50.2 | 50.0 | 61.9 | 72.7 | 72.7 | 80.0 | 49.8 | 51.2 | 57.3 | 87.3 | 96.8 | 75.6 | \columncolorblue!1096.4 |
| FLUX 1 dev | 57.6 | 49.3 | 70.5 | 86.7 | 73.0 | 80.5 | 83.4 | 83.7 | 97.8 | 94.2 | 88.3 | 96.7 | \columncolorblue!1098.5 |
| Kandinsky 3 | 50.2 | 61.4 | 67.7 | 88.2 | 84.0 | 70.2 | 81.4 | 90.1 | 97.8 | 92.8 | 91.2 | 97.7 | \columncolorblue!1099.3 |
| PixArt-$\delta$ | 51.3 | 60.1 | 77.7 | 88.7 | 82.7 | 74.7 | 85.4 | 88.3 | 96.6 | 84.3 | 90.2 | 94.8 | \columncolorblue!1099.0 |
| Juggernaut v11 | 50.1 | 52.9 | 76.0 | 59.8 | 91.3 | 76.8 | 93.2 | 94.0 | 96.6 | 91.3 | 94.0 | 97.2 | \columncolorblue!1099.2 |
| Dreamshaper | 50.2 | 54.4 | 77.3 | 65.2 | 85.4 | 74.8 | 95.6 | 95.2 | 99.4 | 83.1 | 96.9 | 99.9 | \columncolorblue!1098.4 |
| CogView4-6B | 50.0 | 49.9 | 80.5 | 61.5 | 57.5 | 74.1 | 94.6 | 94.8 | 99.1 | 97.6 | 90.4 | 92.9 | \columncolorblue!1099.6 |
| HiDream-I1 | 50.4 | 49.3 | 68.5 | 89.6 | 75.7 | 74.7 | 61.7 | 90.2 | 93.7 | 87.7 | 91.6 | 96.9 | \columncolorblue!1097.9 |
| SD3.5-medium | 50.1 | 56.6 | 76.6 | 72.7 | 74.6 | 79.9 | 80.0 | 87.5 | 94.1 | 95.5 | 86.5 | 92.2 | \columncolorblue!1098.2 |
| MidJourney v7 | 51.2 | 68.0 | 58.2 | 71.0 | 60.2 | 79.7 | 76.5 | 91.8 | 86.6 | 74.6 | 91.6 | 98.0 | \columncolorblue!1087.5 |
| STD | 2.17 | 7.68 | 7.30 | 11.94 | 9.95 | 3.30 | 16.23 | 15.55 | 15.51 | 6.97 | 3.84 | 8.54 | \columncolorblue!103.26 |
| Mean | 51.20 | 56.06 | 70.90 | 75.97 | 76.06 | 77.02 | 78.18 | 83.39 | 89.40 | 89.70 | 91.65 | 92.90 | \columncolorblue!1097.38 |

### 4.4 Robustness to Common Perturbations

A critical attribute of a practical detector is its resilience to image degradations commonly encountered online. We subjected the detectors to two stress tests: JPEG compression and Gaussian blur. The results, shown in Table [3](https://arxiv.org/html/2510.05740v1#S4.T3), highlight a key weakness in many artifact-based detectors. Models that rely on high-frequency spatial artifacts, such as SSP Chen et al. (2024b), NPR Tan et al. (2024) and DNF Zhang & Xu (2023), fail drastically under both compression and blur, despite their high scores on clean images. In contrast, FusionDetect’s performance barely sees any degradation. This remarkable stability confirms that our model’s decisions are based on more fundamental, robust features rather than fragile, easily-disrupted artifacts, making it far more suitable for real-world deployment.

**Table 3: Robustness analysis under common image perturbations. All models are subjected to varying levels of JPEG compression and Gaussian blur. Results are reported as Acc / AP (%).**
|  |  | JPEG Compression | Gaussian Blur |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Detector | No Degradation | QF=95 | QF=75 | QF=50 | $\sigma=1.0$ | $\sigma=2.0$ | $\sigma=3.0$ |
| DNF | 71.61 / 89.12 | 59.83 / 72.44 | 59.80 / 70.77 | 50.89 / 68.18 | 50.89 / 44.23 | 50.88 / 37.10 | 41.96 / 40.96 |
| UNIFD | 63.38 / 72.54 | 61.65 / 70.18 | 61.91 / 70.74 | 61.38 / 70.20 | 59.85 / 69.26 | 59.11 / 68.02 | 58.38 / 66.02 |
| LASTED | 70.35 / 57.58 | 69.43 / 55.94 | 69.83 / 57.53 | 69.14 / 55.28 | 69.34 / 57.68 | 69.12 / 57.19 | 69.82 / 57.22 |
| AIDE | 74.49 / 86.88 | 67.50 / 79.20 | 66.22 / 74.71 | 64.77 / 70.92 | 68.32 / 76.47 | 67.82 / 76.81 | 66.66 / 75.64 |
| NPR | 74.33 / 85.50 | 52.30 / 53.50 | 51.50 / 52.50 | 51.30 / 51.90 | 66.30 / 72.80 | 56.20 / 60.20 | 54.50 / 56.80 |
| SSP | 76.86 / 86.72 | 52.77 / 63.11 | 51.32 / 55.66 | 51.05 / 54.83 | 57.48 / 61.74 | 54.49 / 58.64 | 53.27 / 57.47 |
| \rowcolorblue!10<br>FusionDetect | 80.91 / 90.43 | 80.94 / 91.04 | 81.03 / 91.10 | 80.45 / 91.08 | 80.92 / 92.64 | 79.32 / 92.34 | 78.87 / 92.00 |

### 4.5 Ablation and Sensitivity Analysis

We evaluated the performance of the individual components of our model. As shown in Table [4(a)](https://arxiv.org/html/2510.05740v1#S4.T4.st1), while only using the CLIP Radford et al. (2021) model as feature extractor performs well, consistent with findings from prior work, and only using the DINOv2 Oquab et al. (2023) model is also effective, their fusion in FusionDetect yields the best results. This confirms our core hypothesis that the two models provide complementary features. The t-SNE visualization of these three embeddings is shown in Figure [3](https://arxiv.org/html/2510.05740v1#S4.F3) which indicates that that CLIP+DINO embedding can easily separate not only real and fake images but also their underlying dataset. We also explored incorporating feature up-scaling via FeatUp Fu et al. (2024), but this did not improve performance, suggesting that the raw, powerful features from the foundational backbones are more discriminative for this task.

Moreover, we analyzed the impact of different backbones and classifier depths on performance and the results are shown in Table [4(b)](https://arxiv.org/html/2510.05740v1#S4.T4.st2). The choice of ViT-L/14 for both backbones was made for consistency with prior work and to leverage the power of large-scale architectures. The results show that a relatively simple 4-layer MLP classifier is sufficient to achieve SOTA performance. This indicates that the true power of FusionDetect lies in its robust feature extractor, which is so effective that it does not require a complex classifier to learn a decision boundary.

**Table 4: Ablation and sensitivity analysis of FusionDetect. (a) Ablation on core components. (b) Sensitivity to different backbone and classifier architectures.**
| CLIP | DINOv2 | FeatUp | Acc / AP (%) |
| --- | --- | --- | --- |
| ✓ | ✗ | ✓ | 62.83 / 70.47 |
| ✗ | ✓ | ✓ | 60.69 / 68.38 |
| ✓ | ✓ | ✓ | 66.18 / 72.49 |
| ✓ | ✗ | ✗ | 77.81 / 85.77 |
| ✗ | ✓ | ✗ | 75.42 / 86.11 |
| \rowcolorblue!10<br>✓ | ✓ | ✗ | 80.92 / 92.86 |

Figure: Figure 3: T-SNE Maaten & Hinton (2008) projection of GenImage Zhu et al. (2024), ImagiNet Boychev & Cholakov (2024), and Chameleon Yan et al. (2025) dataset. The CLIP+DINO (bottom) encoder successfully separates real and fake classes for each dataset unlike the other two options. (Top left: CLIP, Top right: DINOv2)
Refer to caption: images/all-emb-vert.png

## 5 Discussion

The empirical findings presented here strongly substantiate our central thesis regarding the ”two-axis generalization” issue. FusionDetect exhibits remarkable stability across the cross-semantic axis, as evidenced by its minimal standard deviation on benchmarks such as GenImage Zhu et al. (2024), ImagiNet Boychev & Cholakov (2024), and Chameleon Zhong et al. (2023). This consistency indicates that its performance is not restricted by the underlying visual domain. Additionally, FusionDetect maintains impressive accuracy on the OmniGen benchmark which can be considered out-of-distribution both semantically and also unseen generators used, affirming its robustness across the two axes. Our proposed benchmark itself plays a pivotal role in this analysis. By incorporating both cutting-edge closed-source models and a variety of community fine-tuned models, it reveals the limitations of detectors that only excel on outdated test sets. These results underscore the necessity of benchmarks that capture both semantic and generator diversity to meaningfully assess a detector’s real-world effectiveness.

## 6 Conclusion

In this paper, we redefined the task of AI-generated image detection by formalizing the “two-axis generalization” task that warrants robustness to both previously unseen generators and different semantic domains. To tackle the two-axis generalization task, we presented the OmniGen Benchmark, a new challenging test set consisting of 12 SOTA generators, and the FusionDetect, a robust detector that solves the two-axis problem by learning representations in a fusion model composed of complementary features extracted from foundational models. We show, empirically, that FusionDetect sets a state-of-the-art within generalization and robustness stages that indicate intelligently fusing complementing features extracted from foundational models is a better paradigm than building from scratch with special architectures. More generally, the two-axis framework offers a valuable method for evaluating model robustness that is broadly applicable, and we hope our contributions lay the groundwork for the next generation of universal fake media detectors.

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## 7 Appendix

### 7.1 Detailed overview of OmniGen Benchmark

This appendix provides supplementary details for the OmniGen Benchmark introduced in Section 3. To offer a comprehensive overview of its composition, Table [5](https://arxiv.org/html/2510.05740v1#S7.T5) lists all 12 state-of-the-art generators used in its creation, along with their respective image counts, resolutions, and sourcing methods. Furthermore, Figure [4](https://arxiv.org/html/2510.05740v1#S7.F4) presents a selection of example images generated by these models, visually demonstrating the high degree of realism and semantic diversity that makes the OmniGen benchmark a challenging and realistic testbed for modern AI image detectors.

Figure: Figure 4: OmniGen Benchmark Images. Top row: Midjourney v7 MidJourney (2025), HiDream Cai et al. (2025), Imagine 4 Saharia et al. (2022), Kandinsky 3 Vladimir et al. (2024); Middle row: Flux 1 Labs et al. (2025), Dreamshaper Lykon (2025), Pixart-$\delta$ Chen et al. (2024c), Cogview 4 Zheng et al. (2024); Bottom row: Juggernaut RunDiffusion (2025), SD3.5 Esser et al. (2024), Imagen 4 ultra Saharia et al. (2022), GPT4o Hurst et al. (2024).
Refer to caption: images/all.jpg

**Table 5: Composition of the OmniGen Benchmark generators containing 11550 synthetic images in total.**
| Generator | Number | Resolution | Gather Method | Real Source |
| --- | --- | --- | --- | --- |
| GPT-4o Hurst et al. (2024) | $550$ | $1024\times 1024$ | Public Dataset | Unsplash |
| MidJourney v7 MidJourney (2025) | $1000$ | $1024\times 1024$ | Public Dataset |  |
| Imagen 4 Saharia et al. (2022) | $1000$ | $1024\times 1024$ | API |  |
| Imagen 4 Ultra Saharia et al. (2022) | $1000$ | $1024\times 1024$ | API |  |
| FLUX 1 Labs et al. (2025) | $1000$ | $1024\times 1024$ | Local Model |  |
| Kandinsky 3 Vladimir et al. (2024) | $1000$ | $1024\times 1024$ | Local Model |  |
| PixArt-$\delta$ Chen et al. (2024c) | $1000$ | $1024\times 1024$ | Local Model |  |
| Juggernaut v11 RunDiffusion (2025) | $1000$ | $1024\times 1024$ | Local Model |  |
| Dreamshaper Lykon (2025) | $1000$ | $1024\times 1024$ | Local Model |  |
| CogView4-6B Zheng et al. (2024) | $1000$ | $1024\times 1024$ | Local Model |  |
| HiDream-I1 Cai et al. (2025) | $1000$ | $1024\times 1024$ | Local Model |  |
| SD3.5-medium Esser et al. (2024) | $1000$ | $1024\times 1024$ | Local Model |  |

### 7.2 Detailed overview of Established datasets

We provide additional information regarding the test sets used for GenImage Zhu et al. (2024), ImagiNet Boychev & Cholakov (2024), and Chameleon Yan et al. (2025) in Section [4](https://arxiv.org/html/2510.05740v1#S4). It includes generators used, number of images, image resolution, semantic categories, and the source for real images utilized in these datasets which is shown in Tables [6](https://arxiv.org/html/2510.05740v1#S7.T6), [7](https://arxiv.org/html/2510.05740v1#S7.T7), and [8](https://arxiv.org/html/2510.05740v1#S7.T8). Note that the numbers reported in these tables indicate the count of fake images only.

**Table 6: Composition of the GenImage Zhu et al. (2024) evaluation set used. The test set contains $4000$ fake and $4000$ real images.**
| Generator | Number | Resolution | Real Source |
| --- | --- | --- | --- |
| BigGAN Brock et al. (2018) | $500$ | $512\times 512$ | ImageNet Deng et al. (2009) |
| VQDM Gu et al. (2022b) | $500$ | $256\times 256$ | ImageNet Deng et al. (2009) |
| SDv1.4 Rombach et al. (2022) | $500$ | $512\times 512$ | ImageNet Deng et al. (2009) |
| SDv1.5 Rombach et al. (2022) | $500$ | $512\times 512$ | ImageNet Deng et al. (2009) |
| Wukong Gu et al. (2022a) | $500$ | $512\times 512$ | ImageNet Deng et al. (2009) |
| ADM Dhariwal & Nichol (2021) | $500$ | $512\times 512$ | ImageNet Deng et al. (2009) |
| Glide Nichol et al. (2021) | $500$ | $512\times 512$ | ImageNet Deng et al. (2009) |
| MidJourney v5 MidJourney (2025) | $500$ | $1024\times 1024$ | ImageNet Deng et al. (2009) |

**Table 7: Composition of the ImagiNet Boychev & Cholakov (2024) evaluation set used. The test set contains $5000$ fake and $5000$ real images.**
| Category | Generator | Number | Resolution | Real Source |
| --- | --- | --- | --- | --- |
| Photos | StyleGAN-XL Sauer et al. (2022) | $388$ | $256\times 256$ | ImageNet Deng et al. (2009) |
| ProGAN Karras (2017) | $424$ | $256\times 256$ | LSUN Yu et al. (2015) |  |
| SD v2.1 Rombach et al. (2022) | $361$ | $768\times 768$ | COCO Lin et al. (2014) |  |
| SDXL v1.0 Podell et al. (2023) | $380$ | $1024\times 1024$ | COCO Lin et al. (2014) |  |
| Paintings | StyleGAN 3 Karras et al. (2021) | $623$ | $1024\times 1024$ | WikiArt Tan et al. (2019) |
| SD v2.1 Rombach et al. (2022) | $131$ | $768\times 768$ | WikiArt Tan et al. (2019) |  |
| SDXL v1.0 Podell et al. (2023) | $129$ | $1024\times 1024$ | WikiArt Tan et al. (2019) |  |
| Animagine XL | $246$ | $1024\times 1024$ | Danbooru Anonymous et al. (2022) |  |
| Faces | StyleGAN-XL Sauer et al. (2022) | $509$ | $1024\times 1024$ | FFHQ Karras et al. (2019) |
| SD v2.1 Rombach et al. (2022) | $295$ | $768\times 768$ | FFHQ Karras et al. (2019) |  |
| SDXL v1.0 Podell et al. (2023) | $288$ | $1024\times 1024$ | FFHQ Karras et al. (2019) |  |
| Other | Midjourney MidJourney (2025) | $626$ | $1024\times 1024$<br>$1792\times 1024$ | Photozilla Singhal et al. (2021) |
| DALL·E 3 Betker et al. (2023) | $600$ | $1024\times 1024$<br>$1792\times 1024$ | Photozilla Singhal et al. (2021) |  |

**Table 8: Composition of the complete Chameleon Yan et al. (2025) dataset. After train/test split, the test set contains $1478$ fake and $1117$ real images. The specific generator used were not reported by the authors.**
| Fake Source | Number | Resolution | Real Source | Category |
| --- | --- | --- | --- | --- |
| Artstation art<br>Civitai civ<br>Liblib lib | $2,976$ | various | Unsplash uns | Scene |
| $2,016$ | Object |  |  |  |
| $313$ | Animal |  |  |  |
| $5,865$ | Human |  |  |  |

### 7.3 Detailed overview of previous detectors

Detectors for synthetically generated images leverage a variety of signals, from low-level artifacts to high-level semantic features. Below is a detailed overview of prominent methods and the core ideas behind their approaches.

- •
NPR Tan et al. (2024): This method focuses on Frequency and Spectral Analysis. It operates on the principle that up-sampling operations in generative models introduce predictable artifacts in the frequency domain. NPR analyzes an image’s frequency spectrum to identify these high-frequency inconsistencies.
- •
DIF Sinitsa & Fried (2024): Also a method based on Frequency and Spectral Analysis, DIF aims to extract a ”Deep Image Fingerprint.” It leverages frequency-aware clues to find unique, model-specific signatures for detection and lineage analysis.
- •
PatchCraft Zhong et al. (2023): This detector is based on Texture and Patch Analysis. It posits that artifacts are more pronounced at a local level and works by analyzing the inter-pixel correlation contrast between rich and poor texture regions within an image.
- •
SSP Chen et al. (2024b): Following the Texture and Patch Analysis approach, SSP operates on local patches and gradients to identify discriminative features that separate real images from generated ones.
- •
DNF Zhang & Xu (2023): This method uses Noise Pattern Analysis. It is designed to extract the unique residual noise patterns present in synthetic images by estimating and analyzing the noise added during the diffusion process.
- •
DIRE Wang et al. (2023): A Diffusion Process-Based Method that relies on reconstruction error. It is founded on the principle that diffusion models can reconstruct their own generated images with significantly lower error than they can reconstruct real-world images. This pixel-space error is used as the primary feature for detection.
- •
AEROBLADE Ricker et al. (2024): This method also uses reconstruction error but measures it in the latent space of the model’s autoencoder, providing a different perspective on the reconstruction fidelity.
- •
LaRE^2 Luo et al. (2024): This approach uses the latent reconstruction error not as a direct feature, but as a guiding signal for a larger, more complex classification network.
- •
DRCT Chen et al. (2024a): A non-error-based diffusion method that employs contrastive training. It cleverly uses the diffusion model’s reconstruction ability to generate hard negative training samples (reconstructed real images labeled as fake) to improve detector robustness.
- •
DistilDIRE Lim et al. (2024): This method addresses the slow speed of error-based detectors by distilling the knowledge from a large, slow DIRE-based detector into a much smaller and faster one, which operates without a full reconstruction cycle.
- •
UniFD Ojha et al. (2023): A pioneering Pretrained Feature-Based detector. It leverages the rich, semantic feature space of large vision-language models like CLIP, demonstrating that a simple linear classifier trained on these general-purpose features can achieve impressive generalization across unseen generators.
- •
LASTED Wu et al. (2023): This detector also leverages vision-language models but through the specific mechanism of language-guided contrastive learning to better align features for the detection task.
- •
Bi-LoRa Keita et al. (2025): This approach creatively reframes the detection problem as an image captioning task. It fine-tunes a VLM to output a simple caption of ”real” or ”fake,” leveraging the model’s generative language capabilities for classification.
- •
AIDE Yan et al. (2025): This detector proposes a Hybrid Model that combines the best of both worlds. It fuses high-level semantic features from a pretrained CLIP model with specialized, hand-crafted modules (like DCT and SRM filters) designed to capture low-level, artifact-based texture statistics.

### 7.4 Additional experiment results

Here we included the supplementary experiment results on our evaluation sets and report AP and accuracy of each class: Real & Fake.

**Table 9: Comparison of detectors on established benchmarks based on real and fake class accuracy. Detectors marked with * were evaluated using their official pretrained weights. Results are in the format: rAcc / fAcc (%).**
| rAcc / fAcc |
| --- |

**Table 10: Comparison of detectors on our proposed OmniGen test set based on real and fake class accuracy. Detectors marked with * were evaluated using their official pretrained weights. Results are in the format: rAcc / fAcc (%).**
| rAcc / fAcc |
| --- |

**Table 11: Comparison of detectors on our proposed OmniGen test set on Average Precision (%). Models marked with * were evaluated using their official pretrained weights. Best results are in bold and second best is underlined.**
| Generator | UNIFD* | BILORA* | LASTED* | UNIFD | AIDE* | LASTED | AIDE | SSP* | SSP | DNF | NPR | DIF* | \columncolorblue!10<br>Ours |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| GPT-4o | 58.7 | 58.1 | 72.0 | 81.2 | 90.4 | 75.9 | 85.3 | 99.8 | 99.9 | 99.7 | 100 | 99.7 | \columncolorblue!1099.7 |
| Imagen 4 | 46.1 | 71.2 | 66.8 | 65.9 | 50.8 | 93.8 | 53.3 | 87.2 | 92.5 | 100 | 99.8 | 100 | \columncolorblue!1099.8 |
| Imagen 4 Ultra | 46.7 | 71.7 | 56.6 | 64.8 | 50.9 | 93.6 | 53.2 | 86.9 | 92.7 | 100 | 99.6 | 100 | \columncolorblue!1099.8 |
| FLUX 1 dev | 39.4 | 72.2 | 68.1 | 73.4 | 94.2 | 79.2 | 93.9 | 99.8 | 99.8 | 100 | 99.9 | 99.4 | \columncolorblue!1099.9 |
| Kandinsky 3 | 63.4 | 63.7 | 79.7 | 70.7 | 93.2 | 89.2 | 96.9 | 99.8 | 99.8 | 100 | 99.9 | 100 | \columncolorblue!10100 |
| PixArt-$\delta$ | 60.4 | 67.3 | 74.0 | 84.3 | 93.7 | 87.9 | 96.0 | 99.0 | 99.0 | 100 | 99.8 | 99.9 | \columncolorblue!10100 |
| Juggernaut v11 | 51.1 | 69.0 | 81.8 | 83.1 | 98.1 | 89.7 | 98.7 | 99.5 | 99.8 | 97.8 | 99.9 | 99.9 | \columncolorblue!1099.2 |
| Dreamshaper | 52.5 | 67.3 | 75.7 | 84.0 | 99.0 | 96.7 | 99.1 | 99.9 | 100 | 98.9 | 99.7 | 100 | \columncolorblue!1099.9 |
| CogView4-6B | 48.5 | 66.8 | 72.8 | 91.5 | 98.8 | 84.6 | 99.0 | 99.8 | 98.8 | 97.8 | 100 | 100 | \columncolorblue!10100 |
| HiDream-I1 | 37.6 | 67.3 | 70.6 | 71.3 | 71.4 | 84.5 | 96.8 | 98.3 | 99.3 | 100 | 99.8 | 100 | \columncolorblue!1099.9 |
| SD3.5-medium | 55.0 | 71.6 | 72.2 | 85.6 | 91.6 | 78.3 | 96.2 | 99.3 | 99.2 | 100 | 99.9 | 100 | \columncolorblue!1099.9 |
| MidJourney v7 | 73.2 | 71.5 | 58.6 | 61.3 | 88.1 | 86.8 | 97.7 | 97.6 | 99.8 | 99.8 | 98.9 | 99.98 | \columncolorblue!1098.3 |
| STD | 10.18 | 4.12 | 7.48 | 9.72 | 17.55 | 6.50 | 17.02 | 4.82 | 2.72 | 0.90 | 0.30 | 0.18 | \columncolorblue!100.51 |
| Mean | 52.72 | 68.14 | 70.74 | 76.42 | 85.03 | 86.68 | 88.83 | 97.24 | 98.38 | 99.49 | 99.77 | 99.91 | \columncolorblue!1099.69 |

## Peer Reviews

confidence: 5
contribution: 1
initial_rating: 2
presentation: 2
questions: 1. How this work is different earlier studies on cross-concept / class?
2. Why it's important? Given social media mostly we're concerned about cross-generator.
soundness: 2
strengths: - The empirical evaluation is extensive, covering multiple established benchmarks plus a new, large benchmark (OmniGen).  
- The method is simple, computationally efficient, and achieves strong quantitative results across generators.
summary: The paper argues that prior work on AI-generated image detection focuses too narrowly on cross-generator generalization and introduces a “two-axis” framework that adds cross-semantic (visual) generalization. It presents *FusionDetect*, a detector that concatenates frozen CLIP and DINOv2 embeddings and trains an MLP classifier, along with a new benchmark (*OmniGen*) containing twelve modern generators.
weaknesses: 1. **Conceptual framing** — The “two-axis generalization” claim overstates novelty.  
   Cross-class or cross-concept evaluation has already been studied (e.g., *Improving Synthetically Generated Image Detection in Cross-Concept Settings*, Dogoulis et al., 2023; *Breaking Semantic Artifacts for Generalized AIGI Detection*, Zheng et al., NeurIPS 2024). These works explicitly examine semantic or scene-level generalization and propose stronger methodological responses than simple dataset aggregation.

2. **Feature-fusion originality** — Combining CLIP and DINOv2 is now mainstream. “Eyes Wide Shut?” (Tong et al., CVPR 2024)  already fuse or compare these backbones. Without a novel fusion mechanism or clear ablation versus stronger CLIP-only or DINO-only heads, the contribution is largely incremental.

3. **Evaluation framing** — Cross-generator generalization is the field’s central challenge with real-world implications (social media, Sora-style video, closed-source APIs). Cross-semantic tests are useful diagnostics but do not justify a redefinition of “generalization.” The paper’s positioning should be moderated to reflect that this is a *comprehensive evaluation* rather than a conceptual overhaul.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: 1. Could you provide more details on the semantic diversity of the OmniGen Benchmark?
2. Can you provide more direct proof to establish the problem of generalizing to unseen semantic content?
soundness: 3
strengths: The paper demonstrates that combining CLIP (for semantic feature) and DINOv2 (for structural features) can create a robust AI image detector with adequate ablations that show the contributions of each of these feature extractors. The authors trained the detector only on SD1.4 and SD2.1 and showed the model generalizes to unseen generators, which is a strong indicator of generalizability.
summary: The paper introduces a two-axis generalization framework for AI-generated image detection (generalization to unseen generators and unseen types of visual content). The authors show that combining CLIP and DINOv2 features is an effective method of detecting AI-generated images. They also present the OmniGen benchmark, which contains 12 modern generators and over 400 different subjects of generation.
weaknesses: One of the primary contributions of the paper is the two-axis generalization framework (generalization to unseen generator and unseen visual content), which leads to the proposal of the OmniGen benchmark. However, the description of the OmniGen benchmark lacks sufficient detail about its unseen visual content. While the authors mention using a prompt template with “over 400 subjects”, they do not provide a categorical breakdown of these subjects or other variables, which makes it difficult to assess its semantic diversity over existing datasets. 

Moreover, it is important to strongly establish the problem of generalizing to unseen semantic content. Further information or citation is required to establish that a detector trained on any existing dataset’s performance noticeably degrades on a single generator across multiple unseen different visual content.

---

confidence: 4
contribution: 2
initial_rating: 2
presentation: 3
questions: Please see the Weaknesses
soundness: 2
strengths: This paper constructs a diverse evaluation dataset and introduces a detection method.
summary: This paper presents a new benchmark that includes 12 state-of-the-art generators to comprehensively evaluate detectors under realistic conditions. Besides, they introduce a new detection method by incorporating two frozen foundation models to achieve a generalizable and robust detection.
weaknesses: 1. Figure 2, as the motivational illustration, does not align with the description of the motivation. Specifically, the figure does not clearly distinguish between the absence of cross-generator overlap and cross-semantic overlap, making it difficult to visually demonstrate how the detector struggles to generalize across these two variables, namely the generator and semantics.
2. The paper identifies the issue that the detector struggles to generalize between the generator and semantics. However, the experimental section does not demonstrate the relationship between the evaluation datasets and these two variables, making it impossible to substantiate the effectiveness of the detection method with respect to both generator and semantics. For example, the results in Table 1 only show the performance of different methods across three distinct test sets, but how these datasets relate to the generator and semantics variables remains unclear.
3. Some of the methods in Tables 1 and 2 use their pre-trained weights, which makes the comparison unfair. It is likely that the higher generalization performance of these methods is due to the more diverse training data used in the paper. To ensure a fair comparison, all methods should be trained on the same unified training set.
4. The paper uses images from the Unsplash platform as real images for the test set and generates fake images based on these. However, how can it be guaranteed that the images from this platform are indeed real? There seems to be no assurance that the images from this platform are authentic.

---

confidence: 4
contribution: 2
initial_rating: 2
presentation: 2
questions: Question: 

As described in Weakness.
 
Suggestions:
1. The title is somewhat misleading: “A TWO-AXIS FRAMEWORK” suggests that the model produces separate outputs for the two axes, whereas it in fact delivers only a single binary prediction.
2. Additional experiments that quantify how differences in visual domains influence generator training should be provided to substantiate the paper’s core claim.
3. The layout of Figure 3 is a disaster; the plots should be merged into a single row, with labels positioned directly beneath them.
soundness: 3
strengths: 1. This paper proposes a previously overlooked challenge and casts it through a two-axis formulation that jointly considers generator shift and semantic-domain shift. 

2. This paper introduce a novel approach, FusionDetect, which achieves state-of-the-art performance.

3. A curated image dataset encompassing state-of-the-art generators is presented, providing a catalyst for continued progress in the field.
summary: The paper re-defines “generalization” for fake-image detection as a two-axis problem: detectors must perform well on both unseen generators and unseen visual domains. To evaluate this rigorously, the authors introduce OmniGen, an open-source benchmark that unites 12 state-of-the-art text-to-image models. They further propose FusionDetect, a lightweight yet powerful detector that concatenates frozen CLIP and DINOv2 features. Extensive experiments show that FusionDetect surpasses 8 recent competitors by 3.87\% average accuracy on legacy datasets and by 4.48\% on OmniGen. FusionDetect remains robust under common corruptions such as JPEG compression and Gaussian blur, dramatically outperforming artifact-based rivals. Moreover, it displays the smallest cross-domain volatility, underscoring its superior adaptability to unseen generators and visual domains.
weaknesses: 1. The paper fails to establish that generalization across visual domains is actually challenging. The proposed method performs very well on OmniGen, suggesting that the task is not hard for a trained model. Current synthetic datasets (such as WildFake [1] and DiffusionForensics [2]) are already semantically diverse enough that modern detectors rarely over-fit to specific objects. Moreover, the domain shift typically manifests as a move from natural photographs to paintings, oil canvases, or pixel art, none of which are represented in Figure 4. So I think the proposed dataset fails to demonstrate a meaningful train-test gap. 

2. The method’s novelty is quite limited: it merely concatenates features from two off-the-shelf backbones. Would performance climb further if additional encoders—BLIP, SigLIP, or others—were fused in the same way?

3. The CLIP and DINOv2 features shown in Figure 3 appear to be used without any MLP training; comparing these raw embeddings with the fused, task-tuned representation is therefore unfair.

[1] Yan et al. Wildfake: A large-scale and hierarchical dataset for ai-generated images detection. AAAI 2025. 

[2] Jeongsoo et al. Community forensics: Using thousands of generators to train
fake image detectors. CVPR 2025.


Decision: Reject