Title: MLEP: Multi-granularity Local Entropy Patterns for Generalized AI-generated Image Detection
Venue: NeurIPS 2025
Accepted: yes
Keywords: AI-generated image detection

entropy

multi-granularity

deepfake detection
Abstract: Advances in image generation technologies have raised growing concerns about their potential misuse, particularly in producing misinformation and deepfakes. This creates an urgent demand for effective methods to detect AI-generated images (AIGIs). While progress has been made, achieving reliable performance across diverse generative models and scenarios remains challenging due to the absence of source-invariant features and the limited generalization of existing approaches. In this study, we investigate the potential of using image entropy as a discriminative cue for AIGI detection and propose Multi-granularity Local Entropy Patterns (MLEP), a set of feature maps computed based on Shannon entropy from shuffled small patches at multiple image scales. MLEP effectively captures pixel dependencies across scales and dimensions while disrupting semantic content, thereby reducing potential content bias. Based on MLEP, we can easily build a robust CNN-based classifier capable of detecting AIGIs with enhanced reliability. Extensive experiments in an open-world setting, involving images synthesized by 32 distinct generative models, demonstrate that our approach achieves substantial improvements over state-of-the-art methods in both accuracy and generalization. Our code and models are available at https://www.github.com/fkeufss/MLEP/.

MLEP: Multi-granularity Local Entropy Patterns for Generalized AI-generated Image Detection

Abstract

Advances in image generation technologies have raised growing concerns about their potential misuse, particularly in producing misinformation and deepfakes. This creates an urgent demand for effective methods to detect AI-generated images (AIGIs). While progress has been made, achieving reliable performance across diverse generative models and scenarios remains challenging due to the absence of source-invariant features and the limited generalization of existing approaches. In this study, we investigate the potential of using image entropy as a discriminative cue for AIGI detection and propose Multi-granularity Local Entropy Patterns (MLEP), a set of feature maps computed based on Shannon entropy from shuffled small patches at multiple image scales. MLEP effectively captures pixel dependencies across scales and dimensions while disrupting semantic content, thereby reducing potential content bias. Based on MLEP, we can easily build a robust CNNbased classifier capable of detecting AIGIs with enhanced reliability. Extensive experiments in an open-world setting, involving images synthesized by 32 distinct generative models, demonstrate that our approach achieves substantial improvements over state-of-the-art methods in both accuracy and generalization. Our code and models are available at https://www.github.com/fkeufss/MLEP/ .

1 Introduction

The rapid development of generative technologies has transformed image synthesis, with models like GAN [1], diffusion model [2], and their variants achieving impressive realism. While enabling new applications in creative industries, these advancements have also raised concerns over misuse in misinformation and deepfakes [3, 4], prompting an urgent need for reliable AI-generated image (AIGI) detection methods. Researchers have leveraged spatial [5, 6, 7, 8] and frequency-domain cues [9, 10, 11, 12], as well as high-level knowledge from pretrained diffusion models [13, 14] and LLMs [15, 16, 17] for AIGI detection. Yet, the lack of source-invariant representations still limits the cross-domain detection robustness, especially when across different models and content types.

To address this challenge, we aim to identify a generalized, content-agnostic pattern that can reliably distinguish AIGIs from real photographs. Inspired by recent studies [7, 8], our work builds on two key observations. Tan et al.[7] found that generative models typically involve internal upsampling operations and propose Neighboring Pixel Relationships (NPR) to capture resulting structural artifacts. However, NPR operates on small local patches and retains visible semantic structures, introducing bias that may hinder generalization. Zheng et al.[8] emphasized the impact of 'semantic artifacts' on detection and propose disrupting image semantics by shuffling 32 × 32 patches. While this improves

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cross-scene generalization, the relatively large patch size still preserves semantic information. We argue that such artifacts persist and continue to limit content-agnostic detection.

Through large-scale subjective observation, we noticed a distinct 'glossy and smooth' texture in AI-generated images, prompting an investigation into their entropy characteristics, a statistical measure of pixel randomness [18]. We conducted a preliminary study comparing local entropy distributions (using 2 × 2 patches) between real and AI-generated images. As shown in Fig. 1, real images consistently exhibit a higher probability of maximum entropy (2.0), suggesting the potential of entropy as a discriminative feature for AIGI detection. Motivated by this, we propose using image entropy as an alternative to pixel differences as proposed by NPR [7]. Entropy captures pixel relationships while reducing semantic dependency by focusing on pixel value distributions rather than contrasts. To further suppress semantic artifacts, we adopt fine-grained patch shuffling (smaller than the 32 × 32 patches used in [8]), which also reduces the computational overhead of entropy computation. Additionally, we incorporate multi-scale resampling and an overlapping sliding window to enhance the granularity of entropy patterns. Our contributions are summarized as follows:

To the best of our knowledge, it is the first attempt to explore the potential of image entropy as a cue for detecting AI-generated images. Using image entropy not only enhances detection accuracy and generalization compared to state-of-the-art methods but also highlights intrinsic differences between real and AI-generated images in terms of pixel randomness, as quantified by image entropy.

We propose M ulti-granularity L ocal E ntropy P atterns ( MLEP ), a set of feature maps with entropy computed from shuffled small patches across multiple resampling scales. MLEP effectively disrupts image semantics to mitigate content bias, while capturing pixel relationships across both spatial and scale dimensions. Using MLEP as input, a standard CNN classifier can be trained for robust and generalized AIGI detection.

Extensive quantitative and qualitative analyses validate the effectiveness of the MLEP design, showing significant improvements over state-of-the-art methods across multiple AI-generated image datasets.

2 Related Work

Spatial-domain Detection Spatial-domain methods typically rely on handcrafted spatial features, local patterns, or pixel statistics to distinguish between real and generated images. The representative methods include generalized feature extraction from CNN-based model [5] and inter-pixel correlation between rich and poor texture regions [6]. Tan et al. [7] observed that upsampling operations are prevalent in image generation models and proposed utilizing neighboring pixel relationships (NPR), computed through local pixel differences, as a simple yet effective cue for AIGI detection. Zheng et al. [8] discovered that image semantic information negatively impacts detection performance and proposed a simple linear classifier that utilizes image patch shuffling to disrupt the original semantic artifacts. Cozzolino et al. [19] proposed a zero-shot detection method that models the distribution of real images using lossless coding with multi-resolution prediction, identifying AI-generated images by detecting higher-than-expected coding costs that indicate deviations from real-image

statistics. Yang et al. [20] proposed Discrepancy Deepfake Detector (D 3 ), which enhances crossgenerator generalization by introducing a parallel branch that extracts a discrepancy signal from distorted features to complement the original representation, achieving better robustness without compromising in-domain performance.

Frequency-domain Detection To tackle the subtlety of spatial artifacts in AI-generated images, frequency-domain methods analyze image frequency components, enabling more effective real vs. fake differentiation. The study in [9] found that GAN-generated images exhibit generalized artifacts in discrete cosine transform (DCT) spectrum, which can be readily identified. Qian et al. [10] proposed a face forgery detection network based on frequency-aware decomposed image components and local frequency statistics. Luo et al. [11] proposed a feature representation based on high-frequency noise at multiple scales and enhanced detection performance by integrating it with an attention module. Liu et al. [12] utilized noise patterns in the frequency domain as feature representations for detecting AI-generated images. Tan et al. [21] proposed FreqNet toward detection generalizability, which focuses on high-frequency components of images, exploiting high-frequency representation across spatial and channel dimension.

Detection leveraging Pretrained Models This group of methods aims to derive generalized features for AIGI detection by leveraging the knowledge learned by large models pretrained on extensive datasets. Wang et al. [13] proposed an artifact representation named DIffusion Reconstruction Error (DIRE), which obtains the difference between the input image and its reconstructed object through a pretrained diffusion model. Chen et al. [14] proposed utilizing pretrained diffusion models to generate high-quality synthesized images, serving as challenging samples to enhance the detector's performance. Ojha et al. [15] utilized representations from a fixed pretrained CLIP model as generalized features for detection. Chen et al. [22] introduced ForgeLens, a data-efficient CLIP-ViT framework that enhances generalization to unseen forgeries by using proposed lightweight weight shared guidance module (WSGM) and forgery-aware feature integrator (FAFormer) to guide frozen features toward forgery-relevant information. Zhang et al. [23] proposed VIB-Net, which employs variational information bottlenecks to enforce authentication task-related feature learned from pretrained CLIP encoder, significantly improving generalization across different generative model types. Similar methods such as [16, 17] also leveraged textual information from vision-language models to further enhance detection performance.

3 The Approach

The proposed approach leverages entropy-based feature extraction to analyze local pixel randomness in a multi-granularity, semantic-agnostic manner. It begins by dividing the image into small patches and applying random shuffling to reduce semantic bias. A multi-scale pyramid is then constructed by downsampling and upsampling the scrambled image, introducing resampling artifacts. Local entropy is computed using a 2 × 2 sliding window across the entire image, capturing complexity across intra-block, inter-block, and inter-scale levels. The resulting multi-granularity local entropy patterns (MLEP) are used as input to a standard CNN classifier for distinguishing AI-generated from real images. An overview of the method is shown in Fig. 2, with key components detailed below.

3.1 Semantic Suppression via Patch Shuffling

Inspired by previous work [6, 8] that mitigates semantic bias via patch-based processing, we adopt finer patch shuffling to further disrupt image content. Given an input image X ∈ R H × W × C , we first partition it into patches of uniform size of L × L :

where L is a small integer (typically < 8 ), and H,W are assumed divisible by L . The patches are then randomly permuted, resulting in a visually scrambled image denoted as ˜ X :

where π is a bijection defining the patch permutation. Note that partitioning and shuffling are applied independently to each color channel.

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3.2 Multi-Scale Resampling

Inspired by [7] showing that generative models often use upsampling to produce high-resolution outputs, we propose detecting generation artifacts via multi-scale analysis. We hypothesize that resampling generated images reveals distinctive patterns useful for detection. To this end, we first construct a multi-scale pyramid by resampling the scrambled image ˜ X with scale factors S = { s 1 , s 2 , . . . , s K } , with each scale s k ∈ (0 , 1] applied using an interpolation function Down( · , s k ) :

which are then upsampled back to its original shape using an interpolation function Up( · , H, W ) :

The resulting multi-scale resampling image ˆ X is created by concatenating all the upsampled images along the channel dimension:

3.3 Multi-granularity Local Entropy Patterns

The core of our approach is the design of Local Entropy Patterns (LEP), which quantify textural randomness using a 2 × 2 sliding window over pixel sets ˆ X i,j = { x m,n } m ∈{ i,i +1 } ,n ∈{ j,j +1 } , based on Shannon's definition of information entropy [18]:

where p ( x m,n ) represents the probability of occurrence of the pixel value x m,n within that specific patch ˆ X i,j . By restricting the sliding window to 2 × 2 (four pixels), entropy values are confined to five discrete levels: V = { 0 , 0 . 8 , 1 . 0 , 1 . 5 , 2 } , as shown in Fig. 3c. The proof and an efficient computation algorithm for LEP on a 2 × 2 window are provided in the supplementary material. With a stride of 1, the 2 × 2 sliding window introduces overlap in LEP computation. Due to patch shuffling, this captures both intra-patch and inter-patch entropy-reflecting local randomness within and across original image regions-as illustrated in Fig. 3b. Applied across multiple scales, LEP further captures inter-scale entropy, forming the basis of the final Multi-granularity Local Entropy Patterns (MLEP).

Given the computed MLEP feature maps denoted as ¯ X ∈ V ( H -1) × ( W -1) × ( C · K ) , a representative CNN-based classifier can be trained to differentiate between photographic and AI-generated images. Denoting the classifier as f , the training objective is defined using the binary cross-entropy loss:

where y i represents the true labels, f ( ¯ X i ) the predictions, and N the number of training samples.

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(c) Five possible LEP values corresponding to different pixel occurrences within a 2 × 2 window.

4 Experiments

4.1 Experimental Settings

Datasets We adopt the cross-dataset setup from [7], using the ForenSynths [5] dataset for training, which includes 20 content categories with 18,000 ProGAN [24] generated images and an equal number of real images from LSUN [25]. Following [7], we train only on four categories: cars , cats , chairs , and horses , posing a challenging cross-scene setting. Following [5, 13, 7, 8], we evaluate on synthesized images from 32 image generation models (16 GAN-based and 16 Diffusionbased, including variants). The GAN-Set includes ProGAN [24], StyleGAN [26], StyleGAN2 [27], BigGAN [28], CycleGAN [29], StarGAN [30], GauGAN [31], AttGAN [32], BEGAN [33], CramerGAN [34], InfoMaxGAN [35], MMDGAN [36], RelGAN [37], S3GAN [38], SNGAN [39], and STGAN [40], with the former seven obtained from the dataset ForenSynths [5] and the latter nine from the dataset GANGen-Detection [41]. The Diffusion-Set contains DDPM [2], IDDPM [42], ADM[43], LDM [44], PNDM [45], VQ-Diffusion [46], Stable Diffusion (SD) v1/v2 [44], DALL·E mini [47], three Glide [48] variants 2 , and two LDM [44] variants 3 . Of these models, the first eight are sourced from the DiffusionForensics dataset [13], while the remainder are from the UniversalFakeDetect dataset [15]. Furthermore, we include images from two commercial models, Midjourney and DALL·E 2, sourced from the social platform Discord 4 as provided by [7]. Each above AIGI subset comprises an equal number of real samples paired with the corresponding generative counterparts. All test images were obtained according to the instructions provided by [7].

Implementation details All images were resized to 224 × 224 , with random cropping for training and center cropping for testing. Multiple variants of the patch size ( L ), resampling scales ( S ), and the classifier backbone were tested with results shown in the ablation study. The training was performed using the Adam optimizer (learning rate of 0.002, batch size of 64). All experiments ran on a server with two NVIDIA RTX A5000 GPUs.

Baseline methods We compare against representative baselines, including CNNDet [5], F3Net [10], LGrad [49], UnivFD [15], CLIPping [16], NPR [7], Zheng [8], FreqNet [21], FatFormer [17],

ForgeLens [22], D 3 [20] and VIBAIGC [23]. Accuracy (Acc.) and average precision (A.P.) are used as metrics. Following the same protocol, we re-evaluated CLIPping [16], Zheng [8], FreqNet [21], FatFormer [17], ForgeLens [22], D 3 [20], and VIBAIGC [23] using their official open-source implementations, while results for the remaining baselines were taken from [7].

4.2 Overall Evaluation of Detection Generalizability

We evaluated the generalization performance of our AIGI detection method across datasets. Accuracy (Acc.) and average precision (A.P.) compared to state-of-the-art GAN- and Diffusion-based methods are reported in Tables 1 and 2, using patch size L = 2 , resampling scales S = { 1 , 1 / 2 , 1 / 4 } , with a

ResNet-50 backbone, which yield the optimal validation results. MLEP consistently achieves top performance across most datasets. Remarkably, it generalizes well to diffusion-generated images, despite being trained solely on GAN-based data (ProGAN [24]). Even on datasets with entirely different content (e.g., face-centric sets like StarGAN, InfoMaxGAN, and AttGAN), MLEP maintains strong performance, underscoring its cross-scene robustness. Table 3 further shows that MLEP outperforms NPR [7], with average gains of 3.1% in Acc. and 1.6% in A.P., despite NPR's already strong results.

4.3 Ablation Study

We next conducted a series of ablation studies to evaluate the effectiveness of key components and hyperparameters in the proposed approach.

Effectiveness of patch shuffling and multi-scale resampling We first assessed the impact of two key components: patch shuffling and multiscale resampling. Ablation results in Table 4 show that removing either component noticeably reduces performance, with patch shuffling contributing more. Even without both, LEP alone achieves over 94.3% accuracy, higher than NPR (94.0%) [7], highlighting the effectiveness of entropybased features.

Impact of the resampling interpolation method We also evaluated the impact of interpolation methods, comparing bilinear, bicubic, and nearestneighbor (Table 5). Bilinear outperforms nearest-neighbor and performs comparably to bicubic. This might be because bilinear and bicubic blend neighboring pixel values, introducing richer entropy variations, while

nearest-neighbor simply copies pixel values, resulting in limited entropy diversity.

Impact of patch size and scale factors We further examined the effects of patch size and resampling scales by testing different hyperparameter settings, as shown in Table 6. The best performance was achieved with the smallest patch size ( l = 2 ), indicating that stronger semantic scrambling improves detection. Moderate multi-scale fusion ( S = { 1 , 1 / 2 , 1 / 4 } ) also led to optimal results, confirming the benefit of in-

corporating resampling artifacts.

Impact of sliding window stride We evaluated the effect of stride in the 2 × 2 sliding window for LEP computation, testing strides of 1 and 2 (Table 7). A stride of 1 significantly outperforms 2, highlighting the importance of interblock entropy in MLEP. This supports our multigranularity design, which captures both intraand inter-block texture patterns as depicted in Fig. 3.

Compatibility with various backbones Lastly, we assessed the compatibility of MLEP with various ResNet backbones [50], including ResNet-18, 34, 50, and 101. As shown in Table 8, all variants achieved strong performance, with slight gains from larger models. This confirms the generality and scalability of the proposed feature extraction method.

Influence of generation model's text prompts In text-to-image diffusion models, the specificity of input prompts may greatly affect the visual quality and details of generated images. To inspect the influence of text prompts on AIGI detection performance, we conducted an additional experiment using DiffusionDB[51], a large dataset with 14 million Stable Diffusion images generated from 1.8 million unique prompts. We randomly selected two subsets of 3,000 images, one set generated from complex prompts (over 200 characters with keywords like 'high quality,' 'detailed,' and 'realistic') and the other from simple prompts (under 100 characters and without those keywords). We evaluated our trained detector on both subsets and found almost no difference in detection accuracy: 99.65% accuracy on the simple set and 99.62% accuracy on the complex set. This further demonstrates the generalizability of the proposed method over different types of AI-generated content.

4.4 Interpretability of MLEP

To illustrate the effectiveness of MLEP for AIGI detection, we conducted a set of qualitative analysis detailed as follows.

Entropy patterns between real and AI-generated images We first visualize LEP maps for several real-fake image pairs, along with their differences in the pixel, entropy, and Fourier domains. Here, 'fake' refers to AI-reconstructed images resembling the originals. Since LEP values are sparse and capped at 2 , we normalize them to [0 , 255] for visualization. As shown in Fig. 4, LEP differences are

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far more pronounced than pixel-level differences, especially for high-quality generations like Stable Diffusion v2, where pixel differences are visually negligible. In the frequency domain, real-fake differences show more consistent patterns than in the pixel space, supporting content-agnostic detection. These results highlight LEP's ability to amplify real-fake discrepancies while minimizing semantic interference.

Semantic suppression capability of MLEP We further examine the semantic suppression capability of MLEP compared to two competitive methods: Zheng [8] and NPR [7]. Fig.5 visualizes feature maps of LEP and MLEP (without multi-scale resampling), alongside those from Zheng [8] and NPR [7]. The 32 × 32 shuffled patches in Zheng[8] still retain noticeable semantic cues both locally and globally. NPR [7] produces edge-like features by computing pixel differences, leaving much of the original semantics intact. In contrast, LEP substantially suppresses semantic content by highlighting pixel-level randomness, and MLEP further eliminates it through fine-grained patch shuffling, enabling learning content-agnostic representation for AIGI detection.

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Feature distribution of real and AI-generated images Finally, Fig.6 visualizes the t-SNE distribution [52] of real and fake samples based on the final feature layer of a ResNet-50 classifier, comparing our method with two competitive baselines-NPR [7] and FreqNet [21]-which also use ResNet-50. We showcase results on four generative models: StarGAN [30], AttGAN [32], ADM [43], and SDv2 [44]. The proposed local entropy patterns (LEP) achieve noticeably cleaner real-fake separation than the baselines, and MLEP further enhances this distinction, demonstrating stronger discriminative capability for AIGI detection.

5 Conclusion and Limitations

This paper explores the use of entropy as a cue for detecting AI-generated images (AIGI) and introduces Multi-granularity Local Entropy Patterns (MLEP), a set of entropy-based feature maps derived from shuffled small patches across multiple image scales. MLEP captures pixel relationships across spatial and scale dimensions while disrupting image semantics, thereby mitigating content bias. Using MLEP as input, a CNN-based classifier (e.g., ResNet) achieves robust and highly generalizable detection performance.

Limitations Nonetheless, limitations still remain. The paper does not explicitly address the robustness of the detector under common image post-processing operations. In fact, when applying different levels of JPEG compression, blurring, or noise, we observe a 17% to 45% drop in detection accuracy-performance that is less satisfactory compared to methods explicitly optimized for robustness. This limitation stems from the fact that MLEP was not specifically designed to handle such transformations, and no special data augmentation techniques were employed during training. Instead, the paper is focused on exploring the potential of using information entropy as a discriminative signal for AIGI detection and on revealing the intrinsic differences in local entropy patterns between real and AI-generated images. Interestingly, the proposed method shows strong robustness to image rescaling: when images are downsampled to half their original resolution, the mean detection accuracy remains above 92.4% (only 4.7% drop). We attribute this to the multi-scale resampling strategy used during training, which effectively introduced resolution variability as a form of implicit data augmentation. Moreover, entropy is computed only within small 2 × 2 windows, as using larger windows would exponentially increase computational complexity. In the future, more efforts could be devoted to improve the robustness and computation efficiency of entropy-based approach.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces MLEP (Multi-granularity Local Entropy Patterns), a novel method for detecting AI-generated images by analyzing local image entropy. Instead of relying on semantic or pixel-level artifacts, MLEP computes entropy features from shuffled small patches across multiple scales, effectively capturing pixel relationships while disrupting image semantics to reduce content bias. A CNN is then trained on these features for robust classification. Experiments across 32 generative models show that MLEP significantly improves both accuracy and generalization, outperforming prior state-of-the-art methods in open-world detection scenarios.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper is the first to propose using image entropy as a core feature for AI-generated image detection. This is a fresh perspective compared to commonly used spatial or frequency cues.\n2. The motivation is clearly explained, building on limitations of previous works (e.g., semantic bias in pixel-based methods) and supporting the idea with preliminary entropy analysis. The Multi-granularity Local Entropy Patterns (MLEP) is a thoughtfully designed contribution that balances semantic disruption and fine-grained pixel relationship capture.\n3. Extensive experiments across 32 generative models in open-world settings demonstrate significant improvements in both accuracy and generalization over state-of-the-art training-free and training-based detectors.\n4. The paper is well-written and easy to follow, with a logical flow from motivation to methodology, experiments, and analysis. Visuals and tables support the claims effectively.\nWeaknesses:\nAlthough the paper shows strong generalization across many generative models, it lacks evaluation on more recent and challenging generators, such as SD3, Flux, etc. I think the authors should test and compare their performance on Synthbuster benchmark (based on RAISE-1k). Including even a small-scale evaluation on these newer sources would provide stronger evidence of real-world applicability and robustness against the latest generation techniques.\n\nQUESTIONS:\nWould you consider evaluating your method on the Synthbuster benchmark, particularly those based on the RAISE-1k dataset? This benchmark includes images generated by some of the most recent and challenging diffusion models, and testing on it—even at a small scale—would provide valuable insights into the practical robustness and generalization ability of your method.\n\nLIMITATIONS:\nNo negative societal impact.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes MLEP for detecting AI-generated images (AIGIs).\nMLEP extracts local pixel randomness as an entropy-based feature, enabling semantic-agnostic, multigranularity discrimination between generated and real images without relying on high-level semantic cues.\n\nTo achieve this, the method employs:\n\nSmall patch shuffling to eliminate semantic clues,\n\nMulti-scale resampling to expose upsampling/downsampling artifacts, and\n\nA 2×2 sliding window to compute local entropy, capturing the statistical relationships between neighboring pixels.\n\nThe extracted MLEP features are fed into a CNN classifier. Experiments show that this approach achieves higher detection accuracy than existing methods across various image generation models.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\nThe observation that the 2×2 window-based entropy values converge into five discrete levels serves as a concise yet original design insight. This differentiates the method from existing approaches such as FFT-based analysis and CLIP-based models.\n\nThe proposed method demonstrates superior performance compared to other baselines on standard benchmarks.\n\nWeaknesses\n\nLack of details on image generation prompts and its implications for MLEP behavior:\nThe paper provides insufficient information about the conditions under which the images were generated (e.g., specific prompts used). In text-to-image diffusion models, not only the content but also the visual detail level varies significantly with prompt specificity (e.g., \"high-quality,\" \"realistic,\" camera model descriptions, etc.).\nWhen simple category names are used as prompts, diffusion guidance often leads to smoothed-out backgrounds, which may be easily detected by MLEP's ResNet module due to low local entropy. This raises concerns about scene generalization, and further discussion or additional experiments on the impact of prompt specificity are needed.\n\nPotential real/fake performance imbalance:\nThe paper should analyze the detection performance for real and fake images separately. Relying solely on AP (Average Precision) may underestimate imbalances between real and fake detection accuracy. A confusion matrix, as well as metrics like FPR (False Positive Rate) and FNR (False Negative Rate), would help clarify whether misclassifications are evenly distributed or skewed toward either class.\n\nLack of robustness tests against image post-processing:\nAlthough robustness is briefly mentioned in the conclusion, experiments involving common post-processing operations such as JPEG compression or noise addition are necessary—especially since these are standard in real-world scenarios and have been evaluated in many AIGI detection studies. The inclusion of such experiments would strengthen the practical relevance of the method.\n\nLimited comparison and outdated baselines:\nWhile ForenSynths is used for consistency with prior work, concerns have been raised about the visual quality of its generated images [1]. Additionally, some baselines are outdated, which casts doubt on the rigor of the performance comparison. Including more recent methods would make the evaluation more convincing.\n\n[1] Gye, Seoyeon, et al. \"Reducing the Content Bias for AI-generated Image Detection.\" 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025.\n\nQUESTIONS:\nIf the following four issues are addressed, the paper's score could be improved:\nIn addition to Weaknesses 1, 2, 3 and 4,\n5. Another entropy-based approach for AIGI detection:\nReference [2] also uses entropy-based features for detecting AI-generated images, but it appears to be missing from the paper's discussion.\nIt would be helpful if the authors could briefly explain the additional contributions or key differences of the proposed MLEP compared to [2].\n[2] Cozzolino, Davide, et al. \"Zero-shot detection of AI-generated images.\" European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2024.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a method for detecting AI-generated images called Multi-granularity Local Entropy Patterns (MLEP). The core idea is to leverage image entropy as a distinguishing feature. \n\nThe method first shuffles small image patches to suppress semantic content, then creates a multi-scale image pyramid through resampling. Finally, it computes local entropy maps across this pyramid, capturing pixel relationships at multiple granularities. These MLEP feature maps are fed into a standard CNN classifier. \n\nThe approach demonstrates significant improvements in both accuracy and generalization for AIGI detection across a wide range of 32 different generative models, outperforming state-of-the-art methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths：\n1  The use of local entropy as the primary cue for AIGI detection is novel and well-motivated. The initial analysis in the introduction provides a clear intuition that real images tend to have higher local entropy.\n\n2. The proposed pipeline, combining fine-grained patch shuffling with multi-scale analysis, is a reasonable approach to suppressing semantic content while amplifying subtle generative artifacts. \n\n3. The method demonstrates excellent generalization capabilities, achieving state-of-the-art performance across different generative models.\n\nWeaknesses:\nI believe the method relies heavily on strong priors, which leads to a lack of high-frequency details in the generated images. I'm interested in understanding how this method performs when applied to recent state-of-the-art high-resolution image generation models, such as GPT-4o and other methods that can produce images at 2K or 4K resolution.\n\nQUESTIONS:\n1. Can the method generalize to recent state-of-the-art ImageNet-scale image generation models such as GPT-4o, Flux, or Imagen 3?\n\n2. Can the method be extended to handle AI-generated video content as well?\n\n\nFor AC：\nPlease note that I’m not very familiar with this specific area, so my comments are offered with that limitation in mind.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 1, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nRecently a various of techniques have been proposed for AI-generated image (AIGI) detection. Many of these approaches rely on spatial-domain cues, frequency-domain cues, or representations from pretrained models. Such methods often lack robustness when applied across diverse generative models and content types. This work introduces a novel approach to mitigate potential biases from model- and content-specific priors by proposing Multi-granularity Local Entropy Patterns (MLEP), which involves computing entropy feature maps across small patches over multiple image. After applying patch shuffling, multi-scale resampling and computing MLEP, a CNN-based classifier is trained for detection. The evaluation experiments demonstrates superior robustness and performance compared to other methods like FatFormer [1, 2]. These findings suggest that MLEP provides a content-agnostic and model-agnostic framework that substantially advances the state of AIGI detection.\n\n*References*\n\n[1] H. Liu, Z. Tan, C. Tan, Y. Wei, J. Wang, and Y. Zhao, “Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection,” in *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*, 2024, pp. 10 770–10 780.\n\n[2] C. Tan, Y. Zhao, S. Wei, G. Gu, P. Liu, and Y. Wei, “Rethinking the Up-Sampling Operations in CNN-Based Generative Network for Generalizable Deepfake Detection,” in *Proceedings of the IEEE/CVF conference on computer vision and pattern recognition*, 2024, pp. 28 130–28 139.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**:\n\n1. The idea of this paper is novel and compelling. By computing entropy within small patches, the proposed MLEP framework captures fine-grained pixel-level differences, introducing a new metric for evaluating AI-generated image (AIGI) detection.\n2. The authors conducted comprehensive experiments on GAN-based models (Table 1) and Diffusion-based models (Table 2), demonstrating the generalization ability of their method.\n3. A thorough ablation study is presented, covering the three key components of the framework (Table 4), the impact of different resampling interpolation methods (Table 6), hyperparameter (Tables 5 and 7), and the effect of different classifier backbones (Table 8).\n4. This paper is well-written and easy-to-follow overall.\n\n**Weaknesses**:\n\n1. The computational cost increases rapidly with larger patch sizes, which limits the scalability of the proposed entropy computation method to larger receptive fields.\n2. Due to the small receptive field, the 2×2 patch may fail to capture broader contextual forgery cues, such as global texture inconsistencies or high-level structural artifacts.\n\nQUESTIONS:\n1. While the authors claim that real images exhibit a higher likelihood of reaching the maximum entropy value (i.e., 2.0), the magnitude of this difference varies significantly across models. Moreover, the distributions at lower entropy levels appear noisy and inconsistent. A more systematic or statistical comparison might strengthen the argument.\n2. While the paper reports strong performance on several generative models such as StyleGAN2 and Stable Diffusion, it remains unclear how well the proposed method generalizes to truly unseen generative models. Additional evaluation on out-of-distribution generators would further strengthen the claims of generalizability.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}