The proliferation of highly realistic AI-Generated Image (AIGI) has necessitated the development of practical detection methods. While current AIGI detectors perform admirably on clean datasets, their detection performance frequently decreases when deployed "in the wild", where images are subjected to unpredictable, complex distortions. To resolve the critical vulnerability, we propose a novel LoRA-based Pairwise Training (LPT) strategy designed specifically to achieve robust detection for AIGI under severe distortions. The core of our strategy involves the targeted finetuning of a visual foundation model, the deliberate simulation of data distribution during the training phase, and a unique pairwise training process. Specifically, we introduce distortion and size simulations to better fit the distribution from the validation and test sets. Based on the strong visual representation capability of the visual foundation model, we finetune the model to achieve AIGI detection. The pairwise training is utilized to improve the detection via decoupling the generalization and robustness optimization. Experiments show that our approach secured the 3th placement in the NTIRE Robust AI-Generated Image Detection in the Wild challenge
Figures & tables
Figure 1: Previous AIGI detection methods mainly consider the generalization performance under different generation models. However, when the images are distorted due to the transmission, the detectors will make false judgments. To make the detector practical in the downstream scenario, it is urgent to consider a robust and generalized detector.
Figure 2: Distorted Images Illustration. Irrespective of whether the images are real or fake, all images are subjected to multiple distortions with high-level strength, such as Gaussian blur, quantization, impulse noise, brighteness, and tone curve transformations.
Dataset
Images
Generators
Distortions
Toy
10,000
10
0
Train
277,000
20
0
Validation-1
10,000
25
5
Validation-2
2,500
25
5
Public test
2,500
30
7
Private test
2,500
35
9
Table 1: Dataset statistics. The validation stage only involves simple distortions. The test stage further includes complex distortions. The distortion in Validation-2 is harder than in Validation-1.
Figure 3: (a) Image Area Statistics. There is a distribution mismatching between the training and validation/test sets. (b) Special Cases from Validation/Test sets. Some images only appear to have local semantic or non-semantic information.
Figure 4: The overall framework of LPT. To achieve robust detection, our strategy involves the simulation of data distribution, the fine-tuning of the visual foundation model, and the pairwise training, respectively.
Type
Distortions
Blur
Gaussian / Lens
Noise
Gaussian / Impulse / Speckle
Color
Shift/ ColorJitter / Moire/ Tone
Brightness
Brighten / Darken
Compression
JPEG / Quantization
Spatial Space
Jitter
Table 2: The introduction of distortions during the model training.
Participants
Public
Private
Test Hard Set
Test Hard Set
MICV
97.38
97.22
Ant International
97.30
97.20
INTSIG
90.78
91.30
vincentlc
86.42
87.33
Reagvis Labs
85.66
86.00
Table 3: Performance comparison on the challenge test set (AUC %). Our approach secures the third placement. ‘ Hard ’ denotes that the results are computed from the distorted images only.
LCE
LKL
LMSE
Validation*
Public Test
✓
-
-
97.31
87.91
✓
✓
-
97.38
88.06
✓
✓
✓
97.79
89.26
Table 4: Ablation study on training strategy (AUC %). Each ✓ indicates that the component is included. ‘*’ means the results are the average from Validation-1 and Validation-2 sets. All results are computed from the clean and distorted images.
Size
Distortion
Val.-1
Val.-2
Public Test
Avg.
-
-
98.89
96.69
89.26
94.95
✓
-
99.17
96.77
89.27
95.07
✓
✓
99.10
96.74
89.58
95.15
Table 5: Ablation study on the data distribution simulation (AUC %). All results are computed from the clean and distorted images.
Figure 5: Left : The impact of the distortion strength. Right : The impact of additional data incorporation.
Model
Validation-1
Validation-2
Public Test
CLIP*
99.50
98.05
92.67
Meta-CLIP2*
99.60
98.44
95.73
EVA-CLIP*
99.22
98.06
95.15
Table 6: Ablation study on the visual foundation model selection (AUC %). Meta-CLIP2 [ 3 ] is the Big-G version, and EVA-CLIP [ 32 ] is the Big-E version. All results are computed from the clean and distorted images. ‘*’ denotes the utilization of data extension.
Methods
Clean
JPEG
Noise
Blur
Avg.
Q=50
k=7 σ =2
v=25
NPR [ 34 ]
91.80
50.20
50.00
65.70
64.42
MLEP [ 41 ]
89.85
48.90
49.90
74.90
65.88
CNN [ 37 ]
74.91
57.57
70.48
74.80
69.44
Effort [ 39 ]
97.22
60.32
68.56
90.88
79.24
DDA [ 1 ]
84.16
78.02
79.65
83.40
81.30
Table 7: Performance comparison (Acc %) on the ForenSynths [ 37 ] . Except the ‘ Avg. ’ column, each result is the average detection accuracy from the eight generation methods (‘ProGAN’, ‘CycleGAN’, ‘Deepfake’, ‘GauGAN’, ‘BigGAN’, ‘StarGAN’, ‘StyleGAN’, and ‘StyleGAN2’). Here, Q, k, σ , and v denote quality factor, kernel size, stand derivation, and variation.
AI-generated image detection identifies synthetic or AI-edited images and develops methods that generalize across generators, domains, and image transformations.
AI-generated image (AIGI) detectors achieve strong accuracy on clean benchmarks, but their performance drops sharply after images are propagated through real-world channels. We trace this fragility to what these detectors actually learn: they overfit to local artifacts left by generators in small spatial neighborhoods, which are easily destroyed by common propagation degradations such as JPEG compression and blur. Instead, we shift the discriminative cue from fragile local artifacts to more robust global structure. Building on this, we propose GlobalForge, a framework with two complementary modules. The Local Information Bottleneck (LIB) suppresses local components to block shortcut learning, while the Global Structural Reasoning (GSR) module forces every token to gather evidence from distant regions. Both modules are trained jointly under a contrastive structural loss based on degradation that keeps the resulting features stable under degradation. To support fine-grained robustness evaluation, we further introduce RealDeg-Bench, covering 7 common degradation operators and multi-step compound chains. GlobalForge improves average BAcc on 8 in-the-wild benchmark groups by 5.89% over the previous state-of-the-art, and is clearly ahead of representative baselines on RealDeg-Bench under both single and compound degradations. Code is available at https://anonymous.4open.science/r/GlobalForge-BE0F/.
Manni Cui, Ruiqi Liu, Dianyuan Zou +8
1Huazhong University of Science and Technology · Institute of Automation, Chinese Academy of Sciences · 3Jilin University +1
Robust detection of AI-generated images in the wild remains challenging due to the rapid evolution of generative models and varied real-world distortions. We argue that relying on a single training regime, resolution, or backbone is insufficient to handle all conditions, and that structured heterogeneity across these dimensions is essential for robust detection. To this end, we propose HEDGE, a Heterogeneous Ensemble for Detection of AI-GEnerated images, that introduces complementary detection routes along three axes: diverse training data with strong augmentation, multi-scale feature extraction, and backbone heterogeneity. Specifically, RouteA progressively constructs DINOv3-based detectors through staged data expansion and augmentation escalation, RouteB incorporates a higher-resolution branch for fine-grained forensic cues, and Route~C adds a MetaCLIP2-based branch for backbone diversity. All outputs are fused via logit-space weighted averaging, refined by a lightweight dual-gating mechanism that handles branch-level outliers and majority-dominated fusion errors. HEDGE achieves 4th place in the NTIRE 2026 Robust AI-Generated Image Detection in the Wild Challenge and attains state-of-the-art performance with strong robustness on multiple AIGC image detection benchmarks.
Fei Wu, Dagong Lu, Mufeng Yao +2
Shanghai Jiao Tong University · INTSIG Information
Detecting AI-generated images (AIGI) remains challenging because detectors often fail to generalize to unseen generators. Although existing methods are trained on large datasets, their performance still degrades when generation settings change, indicating that data scale alone is insufficient and that limited coverage of generative variations during training is a key factor. Studies on generative model editing show that small changes in internal representations can produce diverse and meaningful image variations, many of which are not explored under standard sampling. Leveraging this insight, we propose PROBE (Probing Robustness via Boundary Exploration), a framework that improves detector generalization by actively exploring challenging regions of the generative process. Instead of treating the generator as a fixed data source, PROBE uses the detector as a critic to steer the generator through manifold-level modifications, producing realistic samples that are difficult to classify. These samples expose failure cases that are uncommon under standard data sampling strategies and are used to refine the detector. Experimental results across multiple benchmarks indicate that PROBE enhances generalization to unseen generators, resulting in more generalizable AIGI detection performance. Code and models are available at https://github.com/Amamiya-C/PROBE-AIGI-Detection
Zijie Cao, Weijie Tu, Yao Xiao +3
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China · Australian National University, Canberra, Australia · Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China +1