MLEP: Multi-granularity Local Entropy Patterns for Generalized AI-generated Image Detection
Authors: Lin Yuan
Xiaowan Li
Yan Zhang
Jiawei Zhang
Hongbo Li
Xinbo Gao
Venue: NeurIPS 2025 poster ()
Keywords: AI-generated image detection
entropy
multi-granularity
deepfake detection
Primary area: social_and_economic_aspects_of_machine_learning
TL;DR: This paper proposes a novel AIGI detection method based on Multi-granularity Local Entropy Patterns (MLEP), which captures scale- and location-invariant entropy features to improve accuracy and generalization across diverse generative models.

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
Lin Yuan, Xiaowan Li, Yan Zhang, Jiawei Zhang, Hongbo Li, Xinbo Gad Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing 400065, China yuunlinscqupt. edu. cn, s230201063astu. cqupt edu. cn, fyanzhang1991, zhangjæ, lihongbo, gaoxb}@cqupt edu. cn
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 Al-generated images (AIGIs). While progress has been made, achieving reliable performance across diverse generative models and scenarios remains challenging due to the absence source-invariant features and the limited generalization of existing approaches. In this study, we investigate the potential of using image entropy as a discrimina- tive cue for AIGI detection and propose gullii-grnuulaitty 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 depen- dencies 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 an open-world setting, involving images synthesized by 32 distinct generative models, demonstrate that our approach achieves substantial improve- ments over sfate-of-the-art methods in both accuracy and generalization. Our code and models are available at muthtps:// //www//ww////////////// github. com www///eeffs///MLE
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 misuse in misinformation and deepfakes 3 [4, prompting an urgent need for reliable Al-generated image (AIGI) detection methods. Researchers have leveraged spatial (5] 6 g and frequency-domain [2], 16
cues [9] I0
as well as high-level knowledge from pretrained diffusion models [13] [14 and 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.
LLMs 13
To address this challenge, aim identify generalized, content-agnostic agnostic pattern that reliably distinguish AIGIs from real photographs. Inspired by recent studies [ 8) our work builds on two key observations. Tan et al. found that generative models typically involve internal upsampling operations and propose Neighboring Pixel Relationships (NPR) to capture resulting structural artifacts. However, NPR operates small local patches and retains visible semantic structures, introducing bias that may hinder generalization. Zheng et al. [ emphasized the impact of "semantic artifacts" on detection and propose disrupting image semantics by shuffling 32 32 patches. While this improves
*Corresponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Real
Al-generated
StarGAN
Stable Diffusion
100
100
100
100
80
80
50
50
Peremage
52.4%
45 35.6% 43.9%
64.2%
1%
64.2%
60
60
60
60
40
40
40
20
20
20
2.0
0.0
1.5
2.0
1.5
Entrupy Value
Entropy Value
Entropy Value
Entropy Value
Comparison of local entropy distributions between real and Al-generated images using 2 X 2 patches, with entropy values from (., 1.0, 1.5, 2.0}. Real images consistently show higher likelihood of entropy reaching 2.0.
Figure 1:
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 distinct "glossy and smooth" texture in Al-generated images, prompting an investigation into their entropy characteristics, a statistical measure of pixel randomness [8]. We conducted preliminary study comparing local entropy distributions (using 2 x 2 patches) between real and Al-generated images. As shown in Fig. [ 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 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 fine-graing grained patch shuffling (smaller than the 32 x 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 Al-generated images. Using image entropy not only enhances detection accuracy and generalization compared to state-of-the the-trr methods but also highlights intrinsic differences between real and Al- g-genertted images in terms of pixel randomness, as quantified by image entropy.
We propose Multi Multi-granulani granularity Local Entropy Patterns (MLEP), set of feature maps with entropy computed from shuffled small patches across multiple resampling scales. MLEP effectively disrupts image semantics mitigate content bias, while capturing pixel relationships across both spatial and scale dimensions. Using MLEP as input, 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 Al-generated image datasets.
2 Related Work
Spatial-domain Detection Spatial-domain methods typically rely on handcrafted spatial features, local patterns, or pixel statisties 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. 77] 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 simple linear classifier that utilizes image patch shuffling to disrupt the original semantic artifacts. Cozzolino et al. [99 proposed zero-shot detection method that models the distribution of real images using lossless coding with multi-resolution prediction, identifying Al-generated images by detecting secher-than-expeeee coding costs that indicate deviations from real-in real-image

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statistics. Yang et al. 2 proposed Discrepancy Deepfake Detector (D*), which enhances cross- generator generalization by introducing parallel branch that extracts a discrepancy signal from distorted features to complement the original representation, achieving better robustness without compromising in-domain performance.
Frequency-domaie Detection To tackle the subtlety of spatial artifacts in Al-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. [I0] proposed face forgery detection network based frequency-awar
decomposed image components and local frequency statistics. Luo et al. [T] proposed a feature representation based on high- tigh-freyenncy noise at multiple scales and enhanced detection performance by integrating it with attention module. Liu et al. [12] utilized noise patterns in the frequency domain as feature representations for detecting AI Al-generated images. Tan al. [2] 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 fea- tures for AIGI detection by leveraging the knowledge learned by large models pretrained on extensive datasets. Wang et al. [3] proposed an artifact representation named DIffusion Reconstruction Error (DIRE), which obtains the difference between the input image and its reconstructed object through pretrained diffusion model. Chen et al. [14] proposed utilizing pretrained diffusion models to generate high-quality synthesized images, serving as challenging samples enhance the detector's performance. Ojha et al. [15] utilized representations from fixed pretrained CLIP model as gen- eralized features for detection. Chen et al. [22 introduced Forgel.ens, a data-efficient CL.IP-Vi 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 pre- trained CLIP encoder, significantly improving generalization across different generative model types. Similar methods such as [16] [7] 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 multi-gramullaity, semantic- aggossic manner. 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 x 2 sliding window across the entire image, capturing complexity across intra- block, inter-block, and inter-scale levels. The resulting -ranulggraullarity local entropy patterns (MLEP) are used as input to standard CNN classifier for distinguishing Al-generated from real images. An overview of the method is shown in Fig. with key components detailed below.
3.1 Semantic Suppression via Patch Shuffling
Inspired by previous work 6 g that mitigates semantic bias via patch-based processing, adopt finer patch shuffling to further disrupt image content. Given an input image X E IR Hx XC we first partition it into patches of uniform size of L x L:
x= (Xi. 1LXXXC hisis
where 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:
X = (}m(د)) XiJ H
(2)
where æ is a bijection defining the patch permutation. Note that partitioning and shuffling are applied independently to each color channel.
3

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Partition and Shuffling
Multi-Scall Resampling
Local Entropy Patterns
X
X
LEP
CNN
Fake
Partition
Shufffing
Sliding window: 2x2
Parch Size: Ixl
Down-/ Up-sample
Sliding stride: 1x1
Figure 2: Illustration Al Al-generated image detection using uulti-granularity local entropy patterns (MLEP), which involves three core steps obtain the MLEP feature: Patch Shufiling, Multi-Scale Resampling, and Local Entropy Pattern computation. The resulting MLEP features are then fed into CNN-based classifier (e.g., ResNet) to effectively identify Al-generated images.
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 = {81, (2........; 8K) with each scale 8g (0, 1] applied using interpolation function Down( &k):
X
Down(X, sk),
X
R # X
(3)
which are then upsampled back to its original shape using interpolation function Up(: H, W):
x Up H, W),
x
#xxxC
The resulting multi-scale resampling image X is created by concatenating all the upsampled images along the channel dimension:
X Concat (x (222
x(K),
3.3 Multi-granulan Local Entropy Patterns
The core of our approach is the design of Local Entropy Patterns (LEP), which quantify textural randomness using a 2 x 2 sliding window over pixel sets X,, = (.m.m)me((.--))eeeta based on Shannon's 's definition of information entropy [18]
LEP
Σ p(Fm,,) log, p(mm,n)
(6)
where p(#m..) represents the probability of occurrence of the pixel value Ima, within that specific patch Xt,j By restricting the sliding window to 2 2 (four pixels), entropy values 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
2 x 2 window 1, the 2 x 2 sliding window introduces overlap in LEP computation. Due to patch shufiling, this local randomness within and across original image regions-al as illustrated in Fig. 3b) Applied across multiple scales, LEP further captures inter-scale entropy, forming the basis of the final -ulti-granularity Local Entropy Patterns (MLEP).
provided in the supplementary material. With stride of
captures both intra-patch and inter-patch entropy-reflecee
Given the computed MLEP feature maps denoted as X E ((H--))(W-1)x(C)) CNN-based classifier can be trained differentiate between photographic and Al-generated images. Denoting the classifier f, the training objective is defined using the binary cross-entropy loss:
representative
1 Σ yss log()(X))) + (1 (1) log(1 ((X)))
LBCE
where y, represents the true labels, f(X,) the predictions, and N the number of training samples.

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LEP
X
X
Intru-block LEP
Window: 2x2
Stride: 1x1
2x2
13x(
patch
X11
N&W
shuffle
11
LEP(XL))
C1t 612
M32
Inter-black LEP
(a) I.P computation based a x 2 window sliding over the entire image.
(b) Illustration intra-block and inter- block L,P due to image patch shufffing.
0.8
LEP calculation
Different pixel intensities
(c) Five possible LEP values corresponding to different pixel occurrences within 2x2 window.
Figure 3: Illustration MLEP computation.
4 Experiments
4.1 Experimental Settings
Datasets We adopt the cross-dataset setup from 77. using the ForenSynths (5) dataset for training, which includes 20 content categories with 18,000 ProGAN (24] generated images and equal number of real images from LSUN [25]. Following 77. we train only on four categories: cars, cats, chairs, and horses, posing challenging cross-scene setting. Following [5] 13 7 8
evaluate on synthesized images from 32 image generation models (16 GAN-based and 16 Diffusion-
based, including variants). The GAN-Set includes ProGAN (24), StyleGAN (66.. StyleGAN2 (27).
BigGAN (28), CycleGAN 22) StarGAN (30), GauGAN (I), AttAAN 32) BEGAN (33] Cramer-
GAN [34] InFoMaxGAN
35 MMDGAN (36). ReIGAN [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 21. IDDPM 42)
ADM 43 LDM 44
46). Stable Diffusion (SD) v1/v2 44, DALL-E variants and two LDM variants Of these models, the first eight are sourced from the DiffusionForensics dataset 13 while the remainder are from the UniversalFakeDe-
PNDM [45] VQ-Diffusion
mini 47 three Glide 48
tect dataset [15]. Furthermore, we include images from two commercial models, Midjourney and DALL-E 2, sourced from the social platform Discord 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 x 224, with random cropping for training and center cropping for testing. Multiple variants of the patch size (T), 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 (), F3Net 10
LGrad 49), UnivFD [15), CLIPPing [16). NPR (7). Zheng [8]. FreqNet (1). FatFormer 17
Siide-100-10, Clide-100-23 and Glide-50-27, where Olide-k-I means & steps in the first stage and steps in the second stage of diffusion models. LLDM-200 (LDM with 200 steps) and LDM-200-CFG (LDM with steps with classifier free diffusion guidance).
https: /tt.///d.soooccmm///o

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Table Detection performance in terms of (%c.(%) and A.P.(%) on the GAN-based datasets.
Method
PoGAAN
StyleGAN
StyleGAN2
BigGAN
CycleGAN
StaGAN
GauGAN
AIGAN
Acc.
A.P.
Acc.
A.P.
Acc.
A.P.
Acc.
A.P.
Acc.
A.P.
Acc.
A.P.
Acc.
A.P.
Acc.
A.P.
CNNNDet
91.4
99.4
91.4
76.4
97.5
52.9
73.3
72.7
88.6
63.8
63.9
92.2
51.1
83.7
F3Net 10
99.4
100.0
92.6
99.7
88.0
99.8
65.3
69.9
76.4
84.3
100.0
100.0
58.1
56.7
85.2
94.8
LGrad
49
99.0
100.0
94.8
99.9
96.0
99.9
82.9
90.7
85.3
94.0
99.6
100.0
72.4
79.3
68.6
93.8
Ojha
99.2
100.0
89.0
98.7
83.9
90.5
99.1
87.9
99.8
91.4
1000
89.9
100.0
91.3
Zheng 8
99.7
100.0
90.7
95.3
97.6
99.7
67.0
67.6
85.2
92.6
98.7
100.0
57.1
56.8
79.4
87.7
C1 ping
16
99.8
100.0
94.3
99.4
83.5
98.7
93.8
99.4
95.4
99.9
99.1
100.0
93.4
99.9
91.3
97.4
7
99.8
100.0
99.8
100.0
87.5
95.0
99.5
100.0
86.6
96.2
FregNet 21
99.5
100.0
90.2
99.7
87.9
99.5
90.5
96.0
95.8
99.6
85.6
99.8
93.4
98.6
89.8
98.8
FatForrmrr
17
99.9
100.0
97.1
99.8
98.8
99.9
99.5
100.0
99.4
100.0
99.8
100.0
99.4
100.0
99.3
100.0
ForgeLoom
99,9
100.0
96.7
94.2
99.0
99.8
100.0
99.4
90.1
90.0
D 20
99.4
100.0
94.9
99.2
95.6
99.4
99.1
100.0
92.6
98.5
95.7
99.3
97.9
99.9
84.8
92.9
VIBAIGC
23
99.9
100.0
89.0
98.5
87.0
97.2
95.3
99.1
98.7
99.7
97.7
99.6
99.3
99.9
93
98.1
100.0
99.9
100.0
100.0
100.0
82.0
100.0
100.0
Method
BIGGAN
CramertiAN
InfoMaxCiAN
MMIXIAN
ReKGAN
SAGAN
SNGAN
STGAN
Ace.
A.P.
Acc.
Acc.
A.P.
Acc.
A.P.
Acc.
A.P.
Acc.
A.P.
Acc.
A.P.
Acc.
A.P.
CNNDel (3
50.2
44.9
81..5
97.5
71.1
94.7
72.9
94.4
53.3
82.1
55.2
66.1
62.7
90.4
63.0
92.7
F3Net [0
87.1
97.5
89.5
99.8
67.1
83.1
73.7
99.6
98.8
100.0
65.4
70.0
51.6
95.6
60.3
99.9
LGrrd (p
50.3
71.1
82.0
57.5
99.1
78.0
87.4
68.0
Ojha [S
72.0
98.9
77.6
99.88
77.6
98.9
77.6
99.7
78.2
98.7
85.2
98.1
77.6
98.7
714.2
97.8
Zacang 8
67.4
98.0
74.2
93.8
71.0
93.1
68.4
89.4
98.4
99.9
70.8
69.9
72.4
94.0
92.3
100.0
CLiPping
100.0
100.0
99.7
99.9
98.3
88.4
94.4
99.5
96.4
NP8
99.0
99.8
98.7
99.0
94.5
98.3
98.6
99.0
99.6
100.0
79.0
80.0
88.8
97.4
98.0
100.0
FreqNet 21
98.8
100.0
95.1
98.2
94.5
97.3
95.1
98.2
100.0
100.0
88.4
94.3
85.3
90.5
98.8
100.0
Hathormer
17
98.4
100.0
98.4
100.0
100.0
100.0
98.3
99.9
98.8
9988
ForgeLens
38.4
97.0
87.2
93.1
87.6
92.6
87.5
92.3
92.6
93.1
98.7
99.3
86.7
91.7
90.0
95.2
D 20
$9.5
97.3
95.2
99.2
95.7
99.2
94.6
99.0
91.4
97.5
98.4
99.9
93.8
98.7
93.0
98.5
VIBAICC
23
96.7
99.5
99.0
90.5
96.5
95.3
98.7
94.6
98.4
82.4
92.5
Ours
99.4
100.0
98.5
99.8
98.0
99.8
98.9
99.8
100.0
100.0
83.4
91.7
97.6
99.7
99.9
100.0
Table 2: Detection performance in terms of Acc.(%) and A.P. on the Diffusion-based datasets.
Method
ADM
DDPM
IDDPM
LDM
PNDM
VQ-Diffusion
S1Dv1
SDN2
A.P.
Acc.
A.P.
Acc.
A.P.
Acc.
A.P.
Acc.
A.P.
CNNDet [5]
53.9
71.8
62.7
76.6
50.2
82.7
50.4
75.7
50.8
90.3
50.0
71.0
36.0
76.7
52.0
90.3
F3Net 10
80.9
96.9
84.7
99.4
74.7
98.9
100.0
100.0
72.8
99.5
100.0
100.0
73.4
97.2
99.8
100.0
LGrad 4p
86.4
97.5
99.9
100.0
56.1
92.8
99.7
100.0
69.5
98.5
96.2
100.0
90.4
99.4
97.1
100.0
Ojha
78.4
92.1
72.9
78.8
75.0
92.8
82.2
97.1
75.3
92.5
83.5
97.7
56.4
90.4
71.5
92.4
Zheng 8
72.1
78.9
78.9
80.5
49.9
52.0
99.7
100.0
90.4
96.9
99.6
100.0
54.0
99.7
87.9
96.4
CLiPping
16
78.9
93.8
80.3
85.7
82.4
94,4
90.2
97.6
81.7
93.7
96.3
99.3
58.0
93.1
82.6
94.9
NPR 7
88.6
98.9
99.8
100.0
100.0
FregNet 21
67.2
91.3
91.5
99.8
$9.0
97.3
98.9
100.0
85.2
99.8
100.0
100.0
63.9
98.1
81.8
98.4
Faiformer
1
70.8
93.4
67.2
72.5
69.3
94.3
97.3
100.0
99.5
100.0
100.0
100.0
61.7
96.8
$4.4
98.2
Fargel
69.8
92.4
$2.1
$2.3
62.1
75.8
99.5
100.0
R3.4
97.1
99.5
100.0
93.2
63.2
87.6
20)
89:0
97.8
85.2
94.4
87.7
96.2
88.2
96.4
90.0
96.8
96.1
99.9
98.0
99.8
93.6
98.8
VIBAIGC
23
69.3
$1.8
90.2
97.7
84.6
97.1
56.8
86.5
94.8
99.3
94.2
99.5
60.0
$8.7
58.3
83.2
97.0
99.8
100.0
100.0
100.0
100.0
99.8
100.0
100.0
100.0
100.0
100.0
98.5
100,0
100.0
Method
DALL mini
100-10
Glide 100-27
50-27
LDM-200
LDM
1MM--20-ccE)
Midjournyy
D
Acc.
A.P.
Acc.
A.P.
Acc.
A.P.
Avc.
A.P.
Acc.
A.P.
Acc.
A.P.
Acc.
A.P.
Aoc.
A.P.
CNNDet 5
51.8
61.3
53.3
72.9
53.0
71.3
54.2
76.0
52.0
64.5
$1.6
63.1
48.6
38.5
49.3
44.7
P3Ner 10
71.6
79.9
88.3
95.4
87.0
94.5
88L5
95.4
73.4
83.3
80.7
89.1
73.2
80.4
79.6
87.3
LGrad 29
88.5
97.3
89.4
94.9
87.4
93.2
90.7
95,1
94.2
99.1
95.9
99.2
68.3
76.0
75.1
80.9
Ojha [15
89.5
96.8
90.1
97.0
90.7
97.2
91.1
97.4
90.2
97.1
77.5
88.6
50.0
49.8
66.3
74.6
Zheny
67.9
72.2
79.4
87.8
76.8
84.5
78.2
85,9
81.3
90.1
84.0
91.7
73.2
78.5
$1.4
89.2
CLiPping
16
91.1
98.6
92.0
98 6
91.2
98.8
94.3
99.3
92.8
98.9
77.4
94.3
51.1
50.7
72.3
NPR 7
94.5
99.5
98.2
99.8
97.8
99.7
98.2
99.8
99.1
99.9
99.0
99.8
77.4
$1.9
80.7
83.0
FreqNett 21
97.4
99.8
88.]
96.4
84.5
96.1 1
86.7
96.3
97.5
99.9
97.4
99.9
55.5
65.3
$2.9
61.8
PatFormer
98.8
99.8
94.2
99.2
94.4
99.1 1
94.7
99.4
98.6
99.8
94.9
99.1
62.8
85.4
68.8
93.2
ForgeLeus
[2
99.0
100.0
98.0
99.9
97.5
99.7
98.5
99.8
99.5
99.9
97.7
99.4
77.6
91.7
76.8
94.9
XX
92.8
98.3
98.8
94.9
94.8
99.4
88.3
95.9
92.5
98.6
94.6
VIBAIGC
28
87.8
96.9
87.2
97.6
86.4
97.5
89.2
98.0
95.9
99.4
77.4
92.8
50.4
47.2
55.9
69.4
Ours
95.7
99.9
99.9
100.0
100.0
100.0
99.8
100.0
99.9
100.0
99.8
100.0
87.5
97.1
87.3
97.4
ForgeLens [22
3 20 and VIBAIGC 23
Accuracy (Ace.) and average precision (A.P.) are used
as metrics. Following the same protocol, we re-evaluated CLIPping [16], Zheng [., FreqNet 21]. FatFormer [17], ForgeLens (22], Dd 200, and VIBAIGC [X] 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 reported in Tables I and using patch size L = 2, resampling scales S {1, 1/2, 1/4], with

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Table Mean Acc. and A.P. over GAN-based, Diffusion- and all 32 datasets.
GAN-Set
Diff.-Set
Mean
Method
Acc.
A.P.
Acc.
A.P.
Acc.
A.P.
CNNDet 5
65.4
86.2
51.4
70.7
58.4
78.4
F3Net 10
78.7
90.6
83.0
93.6
80.8
92.1
LGrad 49
78.0
86.9
87.2
95.2
82.6
91.1
Ojha [15
83.2
98.6
77.5
89.5
80.4
94.1
Zheng 8
80.6
89.9
80.9
86.5
80.8
88.2
CLIPping 16
93.9
99.1
81.4
91.5
87.7
95.3
NPR 77]
93.8
97.0
94.2
97.6
94.0
97.3
FreqNet 21
93.1
98.2
81.7
93.8
87.4
96.0
FatFormer [17
99.0
100.0
84.8
95.6
91.9
97.8
ForgeLens
22
93.2
96.2
85.5
93.0
89.4
94.6
D 20
94.5
98.7
91.1
97.7
92.8
98.2
VIBAIGC
23
93.5
98.1
77.2
89.8
85.4
94.0
Ours
96.4
98.2
97.8
99.6
97.1
98.9
ResNet-50 backbone, which yield the optimal validation results. MLEP consistently achieves top performance across most datasets. Remarkably, it generalizes well to diffusion diffusiin-generraee 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 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 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 as- sessed the impact of key ponents: patch shuffling and multi- scale resampling. Ablation results in Table show that removing either component noticeably reduces perfor- mance, with patch shuffling contribut- ing more. Even without both, LEP alone achieves over 94.3% accuracy, higher than NPR (94.0%) (7]. high- lighting the effectiveness of entropy- based features.
Table 4: Ablation study on the impact of key components, where PS represents patch shuffling and MR denotes multi- scale resampling.
GAN-set
Diff.
Mean
LEP PS MR
Acc.
A.P.
Acc.
A.P.
Acc.
A.P.
93.6
94.1 94.9
95.7
94.3 94.9
93.4
94.1
95.8
96.9
94.6
95.5
v
95.7
98.2
97.5
99.6
96.6
98.9
/
96.4
98.2
97.8
99.6
97.1
98.9
Impact the resampling interpola- tion method We also evaluated the impact of interpolation methods, com- paring bilinear, bicubic, and nearest- neighbor (Table 5). Bilinear outper- forms nearest-neighbor and performs comparably to bicubic. This might be because bilinear and bicubic blend neighboring pixel values, introduc- ing richer entropy variations. while nearest-neightee simply copies pixel values, resulting in limited entropy diversity.
Table 5: Impact of the interpolation method.
GAN-set Acc.
-etf.-set
Mean
Interp.
A.P.
Acc.
A.P.
Acc.
A.P.
Bilinear
96.4
98.2
97.8
99.6
97.1
98.9
Bicubic
96.2
97.9
97.9
99.3
96.9
99.1
Nearest
94.6
97.4
96.8
99.2
95.7
98.3
7

---

Impact of patch size and scale factors We further examined the effects of
Table 6: Impact of patch size L and resampling scaling factors S.
GAN-set
Diff.-set
Mean
L
s
patch size and resampling scales by testing different hyperparameter settings, as shown in Table @ The best performance was achieved with the smallest patch size (1 = 2), indicating that stronger semantic scram- bling improves detection. Moderate multi-scale fu- sion (S = (1, 1/4)) also led to optimal results, confirming the benefit of in- corporating resampling artifacts.
Acc.
A.P.
Acc.
A.P.
Acc.
A.P.
{1.1/2)
95.8
97.8
97.5
99.6
96.6
98.7
2
11,1/2
96.4
98.2
97.8
99.6
97.1
98.9
(1,1/2,1/41188)
91.7
97.9
95.3
99.5
93.5
98.7
[1.1/2}
94.5
96.6
95.5
98.8
95.0
97.7
4
(1.1/2,1/4}
94.5
96.8
96.6
99.1
95.5
97.9
(1,1/2,1/4,1/8)
94.2 96.4
96.5
98.8
95.4
97.6
{1.1/2}
93.9
95.8
95.4
97.7
94.7
96.7
8
(1,1/2,1/4)
94.0
96.5
95.8
99.1
94.9
97.8
(1,1/2.1/4.188)
94.4
96.0
95.8
98.1
95.1
97.0
Impact of sliding window stride We eval- uated the effect of stride in the 2 x 2 sliding window for LEP computation, testing strides of I and (Table 7 stride of significantly out- performs 2. highlighting the importance of inter- block entropy MLEP. This supports our multi- granularity design, which captures both intra- and inter-block texture patterns as depicted in Fig.
Table 7: Impact of sliding window stride.
GAN-set Acc. A.P. Acc. A.P. Acc. A.P.
Def
Mean
Stride
96.4 98.2 97.8 99.6 97.1 98.9
2
94.9 97.3 97.0 99.5 95.9 98.4
Compatibility with various back- bones Lastly, we assessed the com- patibility of MLEP with various ResNet backbones [50], including ResNet- 18, 34. 50, and 101. As shown in Table all variants achieved strong performance, with slight gains from larger models. This confirms the generality and scalability of the pro- posed feature extraction method.
Table 8: Evaluation various ResNets.
GAN-set
Deff
Mean
Backbone
Acc. A.P. Acc. A.P.
Acc.
A.P.
ResNet-18
96.0 97.9 97.7 99.5 96.8 98.7
ResNet-34
96.1 98.2 97.7 99.7
96.9 98.9
ResNet-50
96.4
98.2 97.8
99.6
97.1 98.9
ResNet-101
96.4 98.3
97.8
99.6
97.1
99.0
Influence generation model's text prompts 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 Dif.usionDB(51). large dataset with 14 million Stable Diffusion images generated from 1.8 million unique prompts. We randomly seleeted 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 trained detector both subsets and found almost 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 Al-generated content.
4.4 Interpretability of MLEP
To illustrate the effectiveness MLEP for AIGI detection, we conducted a set of qualitative analysis detailed as follows.
Entropy patterns between real and A Al-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 Al-reconstructed images resembling the originals. Since LEP values are sparse and capped at 2, normalize them to [0, 255] for visualization. As shown in Fig. LEP differences

---

Input
LEP
FFI of input
FFT of
Input
LEP
FFI of if
FFT oEP
Ros
Inpur
LPP
FFT of inpur
FFT ofLEP
Input
LEP
FFT of input
FFT LEP
Rcal
Figure 4: Visualization of local entropy patterns for several real-fake image pairs, along with their differences in the pixel, entropy, and Fourier domains.
Origiual
Zheng
APR
MLEP
Figure 5: Qualitative comparison among Zheng 88) NPR (7). and our method. LEP preserves minimal visible semantics, while MLEP (without resampling) further suppresses semantic content.
far more pronounced than pixel-level differences, especially for high-quality generations like Stable Diffusion v2, where pixel differences 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 amplify real- fake discrepancies while minimizing semantic interference.

---

Semantic suppression capabil- ity of MLEP We further ex- amine the semantic suppression capability of MIEP compared to two competitive methods: Zheng (8 and NPR 77. F
FroqNet
NPR
LEP
MLEP
Faur
StarGAN
visualizes feature maps of LEP and MLEP (without multi-scale resampling), alongside those from Zheng [8] and NPR 77] The 32 x 32 shuffled patches in Zheng[8 [ still retain notice- able semantic cues both locally and globally. NPR 7 produces edge-like features by computing pixel differences, leaving much of the original semanties intact. In contrast, LEP substantially suppresses semantic content by highlighting pixel-level random- ness, and MLEP further elim- inates it through fine-grained patch shuffling, enabling learn- ing content-agnoste representa- tion for AIGI detection.
Bod
Xeoi
AIGAN
ADM
SDv2
Figure t-SNE visualization of real vs. fake samples.
Feature distribution of real and Al-generated images Finally, Fig 6 visualizes the t-SNE dis- tribution [52] of real and fake samples based on the final feature layer of ResNet-50 classifier, comparing our method with two competitive baselines-NFF 7 and FreqNet [21] [21]-which which also use ResNet-50. We showcase results on four generative models: StarGAN (30), AuGAN (32), ADM (3),
and SDv2 [.) 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 of entropy introduces Mulli-gramullrrtty 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.
for detecting Al-generated images (AIGI) and
Limitations Nonetheless, limitations still remain. The paper does not explicitly address the ro- bustness 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 that is less satisfactory compared methods explicitly optimized for robust- ness. This limitation stems from the fact that MI.P 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 discriminative signal for AIGI detection and on revealing the intrinsic differences in local entropy patterns between real and Al-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 form of implicit data augmentation. Moreover, entropy is computed only within small 2 x 2 windows, as using larger windows would exponentially increase computational complexity. the future, more efforts could be devoted to improve the robustness and computation efficiency of entropy based approach.
10

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Acknowledgements
This work is supported by the National Natural Science Foundation of China under grants 62201 107, U22A2096, 62502060, 62402073, and 62221005, in part by the Natural Science Foundation of Chongqing under grant CSTB2023SSCQ-L2X00 tion Key R&D Program of Chongqing under grant CSTB2023TIAD-S STX000, and in part by the Science and Technology Research Program of Chongqing Municipal Education Commission under grants KJQN202300666, KJQN202300619, and KJQN202500649, Special thanks are extended to Prof. Nannan Wang, Prof. Xiuli Bi, Prof. Gwanggil Jeon, and Prof. Touradj Ebrahimi for their invaluable guidance, insightful feedback, and continuous encouragement throughout this research.
part by the Science and Technology Innova-
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Reviews:
[{"review_id": "QbI8aNYUtl", "reviewer": "Reviewer_UjYv", "summary": "Recently 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.", "questions": "1. 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.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "Thanks for the rebuttal. My concerns have now been addressed.", "rebuttal": "In response to **Question 1**, we would like to clarify that the entropy comparison shown in Fig. 1 of the manuscript between real and AI-generated images (AIGI) represents an initial observation in our study. It reveals that real images consistently exhibit a higher likelihood of containing 2.0-entropy blocks, suggesting they preserve finer local details compared to AIGI. This aligns with our hypothesis that AI-generated images tend to exhibit greater randomness and less structured detail as quantified by entropy. Motivated by this finding, we further explored the potential of local entropy as a discriminative cue for AIGI detection.\n\nIn response to **Weakness 1**, we acknowledge the scalability limitation of the 2×2 window size. However, this configuration provides maximal computational efficiency, as a 2×2 patch has only five possible entropy states based on pixel occurrence patterns. This allows us to implement a fast, fully vectorized entropy computation method using PyTorch. Specifically, we extract all overlapping 2×2 patches via the unfold function, reshape them into flat vectors, and apply tensor operations to count unique pixel values and assign entropy values through broadcasting or indexing—without any explicit for loops. This approach enables parallel processing across all patches and is highly efficient, particularly when deployed on GPUs. The Python code used for local entropy computation is included in the supplementary materials of our initial submission.\n\nRegarding the concern about using 2×2 local entropy (**Weakness 2**), we emphasize that the core idea of our method is to eliminate high-level image semantics by scrambling small image patches. This design choice intentionally avoids reliance on semantic or content-level information, which may introduce bias, and instead encourages the model to focus on lower-level entropy distributions—capturing information randomness both within local patches and across randomly positioned pixel groups. To complement the 2×2 setting, we further introduce a multi-scale resampling mechanism, which effectively extends the entropy estimation to smaller image scales and enhances detection robustness.\n\n\nFinally, concerning the out-of-distribution evaluation (**Question 2**), we would like to confirm that our experiments were indeed conducted in a cross-generator setting. Our model was trained exclusively on ProGAN-generated images, and tested on images from a range of previously unseen GAN-based and Diffusion model-based generators, thereby validating its generalization across diverse generation methods. To further address your concern, we conducted additional evaluations using image samples generated by previously unseen models, including those produced by GPT-4o, Flux, Stable Diffusion, etc. Specifically, each set of generated images were paired with comparable number of real image samples selected at random, and we tested the model originally trained on ProGAN against these newly introduced datasets. The mean detection accuracy (Acc.)/average precision (A.P.) are reported in the following table, in comparison with the previous SOTA method, i.e. NPR (CVPR 2024). As shown, our method maintains consistently high performance across all these novel datasets, demonstrating its strong generalization ability in out-of-distribution scenarios. \n\n| Dataset | MLEP (Ours) | NPR (CVPR 2024) |\n|:------:|:------:|:------:|\n| GPT-4o | **100.0% / 100.0** | 100.0% / 100.0% |\n| Flux | 84.8% / 96.7% | **94.2% / 98.9%** |\n| firefly (from Synthbuster) | 93.3% / 97.9% | **93.4% / 99.1%** |\n| sdxl (from Synthbuster) | **99.9% / 100.0%** | 99.9% / 100.0% |\n| Midjourney-v5 (from Synthbuster) | 98.7% / **100.0%** | **99.4%** / 99.8% |\n| SD v1.4 (from GenImage) | **98.6% / 99.9%** | 92.3% / 95.6% |\n| SD v1.5 (from GenImage) | **98.7% / 99.8%** | 92.0% / 95.5% |\n| Wukong (from GenImage) | **98.6% / 99.9%** | 87.9% / 92.8% |\n| Mean | **96.6% / 99.3%** | 94.9% / 97.7% |\n\n> The two values in each cell of the above table denote Acc./A.P. respectively."}, {"review_id": "pH2iK3iucw", "reviewer": "Reviewer_3jko", "summary": "This 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.", "questions": "1. 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.", "limitations": "yes", "rating": 4, "confidence": 1, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths：\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.", "was_revised": true, "final_justification": "Thanks for the rebuttal; it effectively solved my question， and I keep my initial positve score", "rebuttal": "Thank you for your valuable comments. Indeed, our work is based on a strong assumption that AI-generated images (AIGIs) may exhibit different levels of high-frequency components or randomness compared to real photographic images. This stems from the observation that generative models primarily focus on producing semantically coherent content, but often fail to replicate the low-level noise characteristics inherent in real imaging processes. This motivated our curiosity to investigate the differences between real and AI-generated images through the lens of information entropy—a widely used metric for quantifying image randomness. To this end, we conducted a preliminary study comparing the local entropy distributions (based on 2×2 pixel patches) of real and AI-generated images. As shown in Fig. 1 of the submitted manuscript, real images consistently display a higher likelihood of containing blocks with an entropy value of 2.0, indicating the presence of finer local details. This finding supports our hypothesis that AIGIs, while semantically plausible, tend to exhibit greater structural randomness and lack the detailed local variability seen in real images. Building on this insight, we proposed MLEP (Multi-granularity Local Entropy Patterns), a method that leverages local entropy as a discriminative signal for AIGI detection. The core idea of MLEP is to suppress high-level semantic cues by scrambling small image patches, thereby forcing the detector to rely on low-level entropy patterns rather than content semantics, which may introduce bias. By analyzing the distribution of information randomness both within local patches and across randomly sampled regions, MLEP operates in a content-agnostic manner. Furthermore, we introduce a multi-scale resampling strategy to generalize the entropy estimation across different resolutions, enhancing the method’s robustness in diverse scenarios.\n\nIn response to your concerns and two questions about the generalization capability of the proposed method on newly emerging, higher-quality generative images, we conducted additional experiments using the suggested datasets, including images generated by GPT-4o, Flux, Midjourney V5, and Stable Diffusion. Specifically, we evaluated the model originally trained on ProGAN against these newly introduced datasets. Each set of generated images were paired with comparable number of real images selected at random. These experiments were thus conducted entirely in out-of-distribution (i.e., cross-generator) settings. The mean detection accuracy (Acc.) and average precision (A.P.) are summarized in the table below. The SOTA method, i.e. NPR (CVPR 2024), are included for comparison as a reference. As shown, our method maintains consistently high performance across all these novel datasets, demonstrating its strong generalization ability in out-of-distribution scenarios. \n\n| Dataset | MLEP (Ours) | NPR (CVPR 2024) |\n|:------:|:------:|:------:|\n| GPT-4o | **100.0% / 100.0** | 100.0% / 100.0% |\n| Flux | 84.8% / 96.7% | **94.2% / 98.9%** |\n| firefly (from Synthbuster) | 93.3% / 97.9% | **93.4% / 99.1%** |\n| sdxl (from Synthbuster) | **99.9% / 100.0%** | 99.9% / 100.0% |\n| Midjourney-v5 (from Synthbuster) | 98.7% / **100.0%** | **99.4%** / 99.8% |\n| SD v1.4 (from GenImage) | **98.6% / 99.9%** | 92.3% / 95.6% |\n| SD v1.5 (from GenImage) | **98.7% / 99.8%** | 92.0% / 95.5% |\n| Wukong (from GenImage) | **98.6% / 99.9%** | 87.9% / 92.8% |\n| **Mean** | **96.6% / 99.3%** | 94.9% / 97.7% |\n\n> The two values in each cell of the above table denote Acc./A.P. respectively.\n\nUnfortunately, our method is less effective on generated video frames, with detection accuracy dropping by more than 30% in most cases. This is primarily because MLEP was not designed to handle image or video compression, and most available video clips have undergone some level of compression. In contrast, our study focused on exploring information entropy as a discriminative signal for AIGI detection, highlighting the intrinsic differences in local entropy patterns between real and AI-generated images, while placing less emphasis on robustness optimization. We fully agree that a robust image-based detector should generalize well to AI-generated video content. However, common video compression techniques can significantly degrade performance by suppressing subtle traces of the generation process—an issue that affects many image-based detectors. Additionally, such detectors are inherently limited in that they cannot capture temporal cues or generation artifacts related to object or scene motion in video. Promising directions for improving video detection include incorporating a wider range of compression operations during training and developing more advanced entropy-based features that also account for temporal information. We plan to investigate these aspects more thoroughly in future work. Thank you for your understanding."}, {"review_id": "TXIbFalvRg", "reviewer": "Reviewer_EVyz", "summary": "This 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.", "questions": "If 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.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\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.", "was_revised": true, "final_justification": "The authors have addressed my concerns and weaknesses, particularly W1, W2, W4, and Q5, with additional experiments and well-reasoned responses. These clarifications resolved my doubts.\nRegarding W3, while the authors acknowledged the limitations I pointed out, they also provided insightful intuitions that helped reinforce the credibility of their approach.", "rebuttal": "## Response to Weakness 1 - Impact of prompts of text-to-image generation models\nThank you for your thoughtful comment. We agree that in text-to-image diffusion models, prompt specificity can greatly affect the visual quality and details of generated images. We acknowledge that our manuscript did not provide enough details about the prompts, as the test images were sourced from public datasets that cover diverse content but do not specify the prompt details for each image.\n\nTo address this, we conducted an additional experiment using DiffusionDB (ACL 2023), 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 each: one from complex prompts (over 200 characters with keywords like “high quality,” “detailed,” and “realistic”) and one from simple prompts (under 100 characters and without those keywords). We evaluated our trained detector on both subsets and found almost no difference in performance (see Table below). This shows that MLEP remains effective even with high-detail prompts, demonstrating its robustness to variations in prompt complexity and image detail.\n| Text Prompt | Acc. (fake only) | \n|------|------|\n| Simple  | 99.65% |\n| Detailed | 99.62% |  \n\n\n\n## Response to Weakness 2 - Potential real/fake performance imbalance \nFirst, we would like to clarify that all of our evaluations were conducted under balanced settings—each set of AI-generated images was paired with a comparable number of real image samples. Therefore, we opted to report standard metrics such as accuracy and average precision, which are commonly used by baseline methods, including NPR. In response to your suggestion, we conducted additional analyses to examine detection performance for real and fake images separately, and further included false positive rate (FPR) and false negative rate (FNR). The results indicate that misclassifications are generally well-balanced, with the FPR being slightly higher than the FNR, suggesting that the model maintains relatively even sensitivity across both classes.\n| Dataset | Acc. | A.P. | Acc.-Real | Acc.-Fake | FNR | FPR | \n|------|------|------|------|------|------|------|\n| GAN-sets | 98.2% | 96.4% | 94.3% | 98.4% | 1.57% | 5.72% |\n| DM-sets | 99.6% | 97.9% | 96.9% | 98.8% | 1.2% | 3.1% |\n| Mean | 98.9% | 97.1% | 95.6% | 98.6% | 1.39% | 4.41% |\n\n\n## Response to Weakness 3 - Lack of robustness tests against image post-processing\nThank you for raising this important point. We acknowledge that the current manuscript does not explicitly address the robustness of our method under common image post-processing operations. Indeed, robustness is not the primary strength of the proposed MLEP approach. For example, when applying different levels of JPEG compression, blurring, or noise, we observed 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 data augmentation techniques were employed during training. Instead, our focus was on exploring the potential of 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, our 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.\n\nWe fully agree that robustness to post-processing is critical for practical deployment. Promising directions for future work include incorporating a broader range of image transformations during training and developing more advanced entropy-based features that are resilient to such distortions. We plan to explore these aspects in greater depth in our subsequent research. Thank you for your understanding.\n\n\n\n## Response to Weakness 4 - Limited comparison and outdated baselines\nThank you for your comments. To address your concern, we conducted additional experiments on several other released generative image datasets, including high-quality images produced by GPT-4o and Stable Diffusion. Specifically, we evaluated our model—originally trained on ProGAN—against these datasets in a fully out-of-distribution (i.e., cross-generator) setting. The results, summarized in the table below, report mean detection accuracy (Acc.) and average precision (AP), with balanced numbers of real and fake samples in each case. As shown, our method consistently delivers strong performance across most tested datasets, demonstrating its robust generalization ability in detecting AI-generated content from previously unseen generators.\n\n| Dataset | MLEP (Ours) | NPR (CVPR 2024) |\n|:------:|:------:|:------:|\n| GPT-4o | **100.0% / 100.0** | 100.0% / 100.0% |\n| Flux | 84.8% / 96.7% | **94.2% / 98.9%** |\n| firefly (from Synthbuster) | 93.3% / 97.9% | **93.4% / 99.1%** |\n| sdxl (from Synthbuster) | **99.9% / 100.0%** | 99.9% / 100.0% |\n| Midjourney-v5 (from Synthbuster) | 98.7% / **100.0%** | **99.4%** / 99.8% |\n| SD v1.4 (from GenImage) | **98.6% / 99.9%** | 92.3% / 95.6% |\n| SD v1.5 (from GenImage) | **98.7% / 99.8%** | 92.0% / 95.5% |\n| Wukong (from GenImage) | **98.6% / 99.9%** | 87.9% / 92.8% |\n| **Mean** | **96.6% / 99.3%** | 94.9% / 97.7% |\n\n> The two values in each cell of the above table denote Acc./A.P. respectively.\n\nMoreover, in response to your suggestion, we compared our method with several competitive approaches recently published in 2025. Using their publicly available pretrained models, we evaluated all methods—including ours—on the same set of GAN- and diffusion-based datasets. The comparison results, reported in terms of accuracy (Acc.) and average precision (AP), are summarized in the table below (with balanced real and fake samples). As shown, MLEP consistently outperforms these state-of-the-art methods, demonstrating its effectiveness and robustness. We hope these results offer a more comprehensive and convincing evaluation of our approach.\n| Baseline |  Venue | GAN-sets |    DM-sets |    Mean |   \n|:------:|:------:|:------:|:------:|:------:|\n| D3 [1] | CVPR 2025 | 94.5% / **98.7%** | 91.1% / 97.7% | 92.8% / 98.2% |\n| VIBAIGC [2] | CVPR 2025 | 93.2% / 98.6% | 77.0% / 88.1% | 85.1% / 93.3% |\n| ForgeLens [3] | ICCV 2025 | 93.2% / 96.2% | 85.5% / 93.0% | 89.4% / 94.6% |\n| **MLEP** | Ours | **96.4%** / 98.2% | **97.8%** / **99.6%** | **97.1%** / **98.9%**|\n\n> Two values in the above table are Acc./A.P. respectively. References:\n    \n    [1] D3: Scaling Up Deepfake Detection by Learning from Discrepancy, CVPR 2025\n    [2] Towards Universal AI-Generated Image Detection by Variational Information Bottleneck Network, CVPR 2025\n    [3] ForgeLens: Data-Efficient Forgery Focus for Generalizable Forgery Image Detection, ICCV 2025\n\n\n\n\n## Response to Question 5 - Differences between MLEP and ZED\nThe key difference between our method MLEP and ZED (Zero-Shot Detection of AI-Generated Images) lies in how they leverage entropy for detecting AI-generated images. The specific differences are given below:\n\n> MLEP detects AI-generated images by explicitly computing Shannon entropy over small pixel patches (typically 2×2) to capture local statistical irregularities. It treats entropy as a spatial signal and uses patch scrambling to eliminate semantic bias. A CNN is then trained on both real and synthetic images to learn discriminative entropy patterns across multiple scales. The core insight is that real images contain more structured high-frequency details, reflected in distinct entropy distributions.\n\n> In contrast, ZED is a zero-shot method trained only on real images. It uses a pretrained lossless encoder to estimate pixel-level probabilities and measures the difference between model-based entropy and negative log-likelihood (NLL)—known as the coding cost gap—as the detection signal. This gap highlights inconsistencies commonly found in AI-generated images.\n\nIt is hard to say MLEP is much more advanced, but the additional contributions of MLEP compared to ZED include:\n\n- Explicit entropy: Computes Shannon entropy over small patches without relying on learned distributions.\n- Multi-scale analysis: Captures entropy patterns across resolutions for better robustness.\n- Semantic-agnostic: Uses patch scrambling to avoid reliance on content semantics.\n- Efficient and lightweight: Uses vectorized ops and standard CNNs for fast, scalable detection.\n- Trainable end-to-end: Optimized directly for detection, unlike ZED’s fixed encoder.\n\nNevertheless, both methods tackle AIGI detection using entropy from different angles, offering complementary insights and demonstrating entropy’s potential as a strong cue for identifying AI-generated images."}, {"review_id": "44YoArDJwr", "reviewer": "Reviewer_YpTU", "summary": "This 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.", "questions": "Would 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.", "limitations": "No negative societal impact.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your positive feedback. In response to your concern about the generalization ability of the proposed approach against more recent generators, we conducted additional experiments on several other released generative image datasets, including high-quality images produced by GPT-4o, Flux, and Stable Diffusion. Specifically, we evaluated our model—originally trained on ProGAN—against these datasets in a fully out-of-distribution (i.e., cross-generator) setting. The results, summarized in the table below, report mean detection accuracy (Acc.) and average precision (A.P.), with balanced numbers of real and fake samples in each case. As shown, our method consistently delivers strong performance across most tested datasets, demonstrating its robust generalization ability in detecting AI-generated content from previously unseen generators. \n\n| Dataset | MLEP (Ours) | NPR (CVPR 2024) |\n|:------:|:------:|:------:|\n| GPT-4o | **100.0% / 100.0** | 100.0% / 100.0% |\n| Flux | 84.8% / 96.7% | **94.2% / 98.9%** |\n| firefly (from Synthbuster) | 93.3% / 97.9% | **93.4% / 99.1%** |\n| sdxl (from Synthbuster) | **99.9% / 100.0%** | 99.9% / 100.0% |\n| Midjourney-v5 (from Synthbuster) | 98.7% / **100.0%** | **99.4%** / 99.8% |\n| SD v1.4 (from GenImage) | **98.6% / 99.9%** | 92.3% / 95.6% |\n| SD v1.5 (from GenImage) | **98.7% / 99.8%** | 92.0% / 95.5% |\n| Wukong (from GenImage) | **98.6% / 99.9%** | 87.9% / 92.8% |\n| **Mean** | **96.6% / 99.3%** | 94.9% / 97.7% |\n\n> The two values in each cell of the above table denote Acc./A.P. respectively."}]

Author rebuttal:

Meta-review: The recommendation is based on the reviewers' comments, the area chair's evaluation, and the author-reviewer discussion. 

This paper studies the use of multi-granularity local entropy as features to train a CNN for classifying real v.s. AI-generated images. All reviewers find the studied setting novel and the results provide new insights. The authors’ rebuttal has successfully addressed the major concerns of reviewers. In the post-rebuttal phase, all reviewers were satisfied with the authors’ responses and agreed on the decision of acceptance.

Overall, I recommend acceptance of this submission. I also expect the authors to include the new results and suggested changes during the rebuttal phase in the final version.
Decision: Accept (poster)