WildAIGC-Bench: Can AI Image Detectors Handle the Wild? A Benchmark of Generated and Edited Images in Online Circulation

Zijian Yu; Hangwei Zhang; Jun Lan; Huijia Zhu; Weiqiang Wang; Jianfu Zhang

cs.CVOct 20, 2026

WildAIGC-Bench: Can AI Image Detectors Handle the Wild? A Benchmark of Generated and Edited Images in Online Circulation

Authors: Zijian Yu, Hangwei Zhang, Jun Lan, Huijia Zhu, Weiqiang Wang, Jianfu Zhang

Organizations: Ant Group · Shanghai Jiao Tong University

Abstract

AI-generated images linked to news events and online claims can mislead audiences, enable fraud, or cause public controversy. We introduce WildAIGC-Bench, built through recurring collection across news websites, fact-checking sources, and related social media content. The pipeline combines broad coverage, timely updates, and human-verified labels through scheduled discovery, automatic screening and preliminary annotation, and human review of image-specific evidence. The benchmark contains 407 verified AI-generated or edited images, each accompanied by a source webpage link and an evidence summary. Images retain the conditions encountered online, including compression, screenshots, and recapture. Evaluation of dedicated visual detectors, multimodal large language models, and a simple Perception Encoder baseline reveals substantial recall gaps relative to existing benchmarks. WildAIGC-Bench provides both a practical detection challenge and a basis for continued data expansion and source-based verification. The dataset is released at https://github.com/YuZijian/WildAIGC-Bench.

Figures & tables

Figure 1: From recurring discovery to verified benchmark releases. Scheduled tasks collect, screen, and annotate candidates across online sources. Human verification and quality control determine inclusion, preserving source evidence and acquired image conditions. Continued collection supports broader releases with more AI images and planned real-image controls.

Figure 2: Representative samples from WildAIGC-Bench: fabricated events, scenes, and products (a–c, e–g); AI-generated fashion and advertising imagery (d, h); and local edits that add fires or replace people (i, j). Sources (linked by panel): (a) Maldita.es ; (b) CRHoy ; (c) AFP ; (d) H&M ; (e) Unexplained Mysteries ; (f) Snopes ; (g) Fact Crescendo ; (h) 4A Advertising ; (i) Lead Stories ; (j) Full Fact .

Figure 3: Resolution and source profile. Left: short-edge distribution of the paired versions. Right: selected source websites, counted per image rather than per event or original platform; remaining sources, including CBS News and DW, are grouped as Other sources.

Visual detectors
	External mean		WildAIGC-Bench			Cropped subset (83)
Method	Recall $\uparrow$	FPR $\downarrow$	Raw	Processed	Drop	Raw	Processed	$\Delta$
DRCT	50.86	12.81	29.48	28.99	21.87	32.53	30.12	-2.41
DDA	81.66	9.48	35.38	36.36	45.30	32.53	37.35	+4.82
AIDE	52.97	13.05	14.50	18.43	34.55	8.43	27.71	+19.28
B-Free	81.17	6.11	33.66	33.17	48.00	24.10	21.69	-2.41

Table 1: External mean recall and FPR, and WildAIGC-Bench recall (%). The cropped subset reports recall before and after cropping on the same 83 images. Drop is external mean recall minus full-set processed recall, in percentage points.

Method	EvalGEN Recall $\uparrow$	Synthb.	UnivFD	AIGI.	Chame.	Synth WildX	WildRF	RRD.	ITW-SM	Mean BAcc $\uparrow$
Visual detectors
DRCT	50.9	76.3 / 3.4	55.2 / 11.5	75.9 / 4.6	69.0 / 20.1	74.0 / 11.7	68.3 / 21.8	60.4 / 15.4	73.0 / 13.9	69.0
DDA	96.9	92.1 / 13.3	84.7 / 14.1	85.0 / 4.5	82.4 / 6.0	90.7 / 11.5	87.7 / 11.8	71.2 / 4.7	87.2 / 10.1	85.1
AIDE	56.3	79.4 / 6.5	79.1 / 22.8	81.1 / 8.6	62.8 / 8.3	67.9 / 12.9	67.8 / 19.5	57.4 / 7.5	62.3 / 18.4	69.8
B-Free	91.3	97.0 / 0.3	86.7 / 9.3	84.8 / 2.2	76.6 / 8.8	95.6 / 7.3	92.8 / 7.6	71.9 / 4.0	89.7 / 9.4	86.9
Effort	54.4	49.6 / 97.8	71.9 / 47.3	72.4 / 38.4	52.4 / 47.1	49.8 / 35.1	70.8 / 27.7	50.7 / 13.6	53.7 / 29.9	58.9

Table 2: External evaluation (%): each paired entry reports BAcc

\uparrow

/ FPR

\downarrow

. EvalGEN reports recall only. Bold marks the best displayed values; shading highlights PE baselines and mean BAcc.

Appendix figures & tables4 assets

Supplementary material from the paper’s appendix.

Appendix

Method	Raw	Processed	$\Delta$	Recoveries	Losses
DRCT	32.53	30.12	-2.41	5	7
DDA	32.53	37.35	+4.82	9	5
AIDE	8.43	27.71	+19.28	18	2
B-Free	24.10	21.69	-2.41	2	4
Effort	26.51	31.33	+4.82	9	5
PGC	16.87	13.25	-3.61	0	3

Appendix

Table A1: Paired results on 83 cropped images. Recall and

\Delta

are percentages and percentage-point changes, respectively; recoveries and losses are image counts.

Benchmark	Visual total	MLLM total	Real	AI
EvalGEN	55,285	2,000	0	2,000
UnivFD	90,329	2,000	1,000	1,000
AIGIBench	210,798	2,000	1,001	999
Chameleon	26,033	2,000	1,142	858
RRDataset	50,991	2,000	1,000	1,000
Synthbuster	4,000	4,000	1,000	3,000

Appendix

Table A2: Evaluation sizes and class composition for MLLMs. EvalGEN contains AI images only.

Method	Model / checkpoint	Input preprocessing
DRCT	ConvNeXt-Base; DRCT-2M / SDv1.4; 14_acc0.9996.pth	224 center crop; ImageNet normalization.
DDA	DINOv2 ViT-L/14 with LoRA; DDA_ckpt.pth	336 center crop; CLIP normalization.
AIDE	ConvNeXt-XXLarge; GenImage_train.pth	RGB and four DCT reconstruction views, each resized to $256\times 256$ ; ImageNet normalization.
B-Free	DINOv2 ViT-B/14 with four registers; model_epoch_best.pth	Native-resolution forward with a five-crop wrapper (center plus four corners) with a crop size of $504\times 504$ pixels; ImageNet normalization. Longest edge downscaled to 2,048 pixels when exceeded.
Effort	CLIP ViT-L/14 with SVD-residual attention, 2-class head; effort_clip_L14_trainOn_sdv14.pth	Shorter side replicate-padded to 256 (no resizing), then 224 center crop; CLIP normalization.
PGC	DINOv2-large with LoRA, 6-channel RGB-plus-quantization-residual input; PGC_train_progan_sdv1_4_ckpt.pth	224 padded center crop, RGB concatenated with quantization residuals to form six input channels; ImageNet normalization.

Appendix

Table A3: Visual detector configurations. Weight filenames are included for the evaluated open-source checkpoints.

Method	Model / access	Image input and decoding	Decision / prompt
FakeVLM	LLaVA-1.5-7B based detector; local FakeVLM weights	Checkpoint processor; RGB input; greedy decoding, up to 64 new tokens.	Real/fake text; P1
BusterX++	Qwen2.5-VL based detector; local BusterX++ weights	Checkpoint processor and vision utility; greedy decoding, up to 512 new tokens.	Boxed A/B answer; P2
Veritas++	Qwen3-VL-8B based detector; merged local weights	Checkpoint processor and vision utility; greedy decoding, up to 1,024 new tokens.	Verdict parser; P3
Qwen3.6-35B-A3B	API access	RGB image submitted as base64 JPEG; temperature 0; thinking disabled; up to 1,024 output tokens.	Verdict parser; P3
Qwen3.8-27B	API access	RGB image submitted as base64 JPEG; temperature 0; thinking disabled; up to 1,024 output tokens.	Verdict parser; P3
GLM-5.3-Flash	API access	RGB image submitted as base64 JPEG; temperature 0; thinking disabled; up to 1,024 output tokens.	Verdict parser; P3

Appendix

Table A4: MLLM settings. P1–P3 identify the prompt descriptions below.

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WildFakeToolBench: Benchmarking AI-Generated Image Detectors with Web-Sourced Image Processing

Mingjie Li, Yukun Jiang, Yicong Tan, Yang Zhang

CISPA Helmholtz Center for Information Security

Adversarial Attacks on AI-Generated Image Detectors AI-Generated Image Detection Black-Box Adversarial Attacks

Open paper AskPDF arXiv

As AI generated images are getting popular these days, discussions on such generations are widely existed in current public forums, software repositories, and photography tutorials. Moreover, with the help of LLM-assisted code translation, malicious actors can adopt such information to evade detectors without specialized knowledge or local GPU execution of the resulting tools. To evaluate such risks, we introduce WildFakeToolBench, a benchmark suite of

156

executable image-processing tools derived from

220

public sources across

142

domains. Each tool retains its source evidence and passes syntax, execution, and output-distortion checks without using feedback from the evaluated detectors. On

105

generated images from five generators, we evaluate six state-of-the-art detectors with images processed by the tools in our collected bench to assess their effectiveness against such in-the-wild risks. The strongest individual tools achieve attack success rates of

25.25\%

100\%

across detectors, exceeding the strongest of six selected image-processing baselines for each detector. Evaluating subsets of an eight-tool pool yields worst-case coverage above

80\%

for every detector. Effectiveness is sparse and detector-specific: high coverage across candidates does not imply reliable one-shot evasion. These findings not only highlight the potential of such web-sourced risks, but also show the potential on establishing AI-image detector robustness evaluation framework based on those in-the-wild information.

Abstract

AI-Generated Image Detection

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