It Takes a Forum: Resources & Stakeholders Sustaining the Online Synthetic Non-Consensual Intimate Image Creation Ecosystem

Bernardo B. P. Medeiros; Malvika Jadhav; Allison Lu; Tadayoshi Kohno; Vincent Bindschaedler; Kevin R. B. Butler

cs.CYApr 14, 2026

It Takes a Forum: Resources & Stakeholders Sustaining the Online Synthetic Non-Consensual Intimate Image Creation Ecosystem

Authors: Bernardo B. P. Medeiros, Malvika Jadhav, Allison Lu, Tadayoshi Kohno, Vincent Bindschaedler, Kevin R. B. Butler

Organizations: University of Florida · Georgetown University

Abstract

Many malicious actors responsible for disseminating synthetic non-consensual intimate imagery (SNCII) operate within internet forums to exchange resources, strategies, and generated content across multiple platforms. Technically-sophisticated actors gravitate toward certain communities (e.g., 4chan), while lower-sophistication end-users are more active on others (e.g., Reddit). To characterize key stakeholders in the broader ecosystem, we perform an integrated analysis of multiple communities, analyzing 282,154 4chan comments and 78,308 Reddit submissions spanning 165 days between June and November 2025 to characterize involved actors, actions, and resources. We uncover a litany of resources both shared and discussed by myriad actors on both forums, including 156 low-ranking adaptation (LoRA) files used to finetune models to target 141 female celebrities (on 4chan), and links to 54 commercial nudification applications (on Reddit). Our results illustrate: (a) that users with differing levels of technical sophistication employ and share a wide range of primary resources facilitating SNCII content creation as well as numerous secondary resources facilitating dissemination; and (b) that knowledge transfer between experts and newcomers facilitates propagation of these illicit resources. Based on our empirical analysis, we identify gaps in existing SNCII regulatory infrastructure and synthesize several critical intervention points for bolstering deterrence.

Figures & tables

ACTOR	ROLE
Genner	Share generated content, respond to content requests.
Educator	Share resources, respond to resource requests.
Socializer	Discuss, repost, and react to shared content/resources; engage in general conversation.
Requester	Request resources and/or generated content.
Promoter	Advertise nudification products.
Veteran	Long-time member; engage in meta discussion.

Table 1. Multi-label behavioral codes for actors and actions involved in the nudification resource-sharing ecosystem on internet forums. Labels are not mutually exclusive.

RESOURCE	DESCRIPTION
Primary Resources
Model	Diffusion model checkpoints for SNCII creation.
LoRA	Fine-tuning layers for training models on targets.
Dataset	Target datasets shared for creating LoRAs.
UI	Interfaces (e.g., ComfyUI, Forge) for model use.
Prompt	Text inputs for image generation.

Table 2. Resources used in forum SNCII communities. Primary resources are directly used in SNCII creation. Secondary resources distribute primary resources and guides for using them. A single submission may contain multiple resources.

Figure 1. Overview of our dual-pipeline methodology. (1) 4chan pipeline: data is collected via web scraping, filtered, manually annotated, then used to train a transformer-based classifier to label the full dataset for knowledge graph construction. (2) Reddit pipeline: data is downloaded via Arctic Shift, filtered for commercial nudification application mentions, checked for referral link patterns, and analyzed at the user and app levels. Both datasets inform a shared multi-label behavioral codebook of actors, actions, and resources. Results jointly characterize the broader ecosystem.

Post Node Attributes
Base	post_id , username , timestamp ,
	text , has_image
Labels	actions[] , actors[] ,
	resources[] , harm_signals[]
Edges (Post $\rightarrow$ Post)	Count
discusses	216,520

Table 3. [4chan] Knowledge graph schema and statistics.

Category	Macro F1	95% CI
Resources	0.92	[0.89, 0.96]
Harm Magnifiers	0.92	[0.88, 0.95]
Actions	0.68	[0.62, 0.74]
Actors	0.85	[0.81, 0.87]

Table 4. [4chan] Silver dataset validation against 200 samples not in the Golden dataset. We report macro-averaged F1 scores with 95% bootstrap confidence intervals.

Figure 2. Primary and secondary ecosystem stakeholders. Primary stakeholders directly participate in the nudification ecosystem. Secondary stakeholders enable primary resource distribution. Stakeholders only identified on one forum are shown in italics .

\blacktriangle

designates stakeholders held accountable by existing U.S. legal infrastructure. Most stakeholders are not held accountable: only those who post SNCII content online and the platforms hosting that content are covered.

Category	Label	Count	Percentage	Resource Label	Count	Percentage
Actions	share_content	111,470	39.51	lora	13,834	4.90
	discuss	101,958	36.14	model	10,009	3.55
	react_praise	21,662	7.68	prompt	6,209	2.20
	request_generation	17,702	6.27	ui	4,088	1.45
	meta	7,049	2.50	file_sharing_service	4,014	1.42
	request_resource	4,975	1.76	hardware_instructions	2,304	0.82

Table 5. [4chan] Silver label distribution (N=282,154 posts).

Figure 3. [4chan] Resource mentions by month. A brief spike in July-August 2025 follows the removal of real-person likeness models from Civitai in May 2025 ( Civitai, 2025 ) .

Figure 4. [4chan] Monthly generation request volume & fulfillment (June 9–November 21, 2025). Request rates are high, but fulfillment is very low, suggesting users desire SNCII but may not receive the generations they desire from others.

Action	Total Posts	Posts Engaged	Total Engagement	Avg	% Engaged
Share_Content	111,470	63,961	105,485	1.65	57.4%
Discuss	101,958	53,939	81,193	1.51	52.9%
Request_Generation	17,702	7,365	11,576	1.57	41.6%
Meta	7,049	4,472	7,342	1.64	63.4%
Request_Resource	4,975	2,734	4,300	1.57	55.0%
Respond_Request	3,625	2,377	4,079	1.72	65.6%

Table 6. [4chan] Engagement by Action. Total Posts : # of posts with specific action. Posts Engaged : # of posts receiving at least one interaction. Total Engagement : sum of received interactions. Avg : average interactions per engaged post (Total Engagement / Posts Engaged). % Engaged : percentage of posts receiving at least one interaction (Posts Engaged / Total Posts

\times

100).

Figure 6. [Reddit] Monthly commercial nudification app mentions. Application A mentions dwindle following terms of service changes in October 2025.

Subreddit	Submissions	Mentions (%)
#1	<1,000	41.53
#2	<100	24.56
#3	<15,000	12.37
#4	<15,000	3.97
#5	<5,000	3.63
#6	<50,000	2.39

Table 7. [Reddit] Subreddits Ranked by Percentage of Posts Mentioning Commercial Nudification Apps. Submissions : # of posts and comments on a subreddit, upper-bounded to prevent reidentification. Mentions : # of submissions that mention one or more commercial nudification apps. Percentage is calculated out of the total submissions to that subreddit.

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Rank	Posts	Engagement	Educator	Genner	Minor
1	2,769	9,419	61	1,556	15
2	1,011	2,956	41	222	3
3	944	925	87	163	11
4	859	1,376	4	268	2
5	537	2,287	17	149	4
6	506	1,170	6	253	3

Appendix

Table 8. [4chan] Top 10 4chan named influencer profiles.

Rank	Activity	Ref (%)	Subreddits	Apps	Days
1	88	100	#6	a, b	8
2	42	100	#6	a, b	1
3	34	69.4	#3, #4, #6	a, b	14
4	31	75.6	#4, #5, #6	a, b	8
5	26	100	#3, #4, #6	a, b	10
6	26	96.3	#3, #6	a	5

Appendix

Table 9. [Reddit] Top promoters by Referral Link Posting Activity. Activity : total # of submissions containing a referral link. Ref (%) : total percent of activity made up by referral link sharing. Days : # of days on which the user posted.

Code Category	Code	Count	Code Category	Code	Count
Actor	Socializer	290	Resource	Resource Type
	socializer	223		lora	69
	socializer_veteran	67		model	49
	Genner	134		file_sharing_service	28
	genner_unknown	64		ui	19
	genner_unknown_veteran	35		prompt	19

Appendix

Table 10. Actor, Action, Resource, and Harm Magnifier Codes (n=400 4chan comments).

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From Celebrities to Anyone: Characterizing AI Nudification Content, Technology, and Community Dynamics on 4chan

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AI nudification uses generative models to create synthetic non-consensual sexually explicit imagery (SNEACI) of real individuals. Prior work has examined dedicated nudification platforms and model repositories, finding that most targets are female celebrities. However, the anonymous content community, where SNEACI is actively requested, generated, and exchanged, remains unexplored. In this work, we present a large-scale study of AI nudification in the wild, identifying 24,105 SNEACI items. We find a significant shift in target demographics: non-celebrity individuals now account for 55.8% of targets, compared to only 4.7% in prior studies, indicating that AI nudification has expanded from targeting public figures to increasingly harming individuals within users' own social circles. Meanwhile, open-source models dominate production, with Stable Diffusion family generating 42.7% of images and Wan generating 66.5% of videos, all driven by thousands of shared fine-tuned models and accessible tutorials. Yet the ecosystem runs on a small cohort of active producers, with the most prolific producing 780 items, drives community engagement, shapes target demographics, and disseminates technical knowledge that lowers barriers for new producers. Our work provides an empirical understanding of how AI nudification operates in the wild, revealing the mechanisms that sustain this ecosystem and highlighting the urgent need for interventions in platform governance, technical safeguards, and affected individual protection.

Abstract

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