Pith. sign in

REVIEW 1 cited by

Characterizing the MrDeepFakes Sexual Deepfake Marketplace

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2410.11100 v3 pith:KH4732AP submitted 2024-10-14 cs.CY cs.CRcs.HCcs.SI

classification cs.CYcs.CRcs.HCcs.SI
keywords sexualdeepfakecreatedeepfakesmarketplaceattackermaterialmrdeepfakes
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

The prevalence of sexual deepfake material has exploded over the past several years. Attackers create and utilize deepfakes for many reasons: to seek sexual gratification, to harass and humiliate targets, or to exert power over an intimate partner. In part enabling this growth, several markets have emerged to support the buying and selling of sexual deepfake material. In this paper, we systematically characterize the most prominent and mainstream marketplace, MrDeepFakes. We analyze the marketplace economics, the targets of created media, and user discussions of how to create deepfakes, which we use to understand the current state-of-the-art in deepfake creation. Our work uncovers little enforcement of posted rules (e.g., limiting targeting to well-established celebrities), previously undocumented attacker motivations, and unexplored attacker tactics for acquiring resources to create sexual deepfakes.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 2 citations worldwide. Full citation record

  1. Consiglieres in the Shadow: Understanding the Use of Uncensored Large Language Models in Cybercrimes

    cs.CR 2025-08 conditional novelty 7.0 of 10

    Using a knowledge graph of model and dataset relationships, this paper identifies more than 11,000 uncensored LLMs on Hugging Face and documents their use in cybercrime services.

Pith tools