Faked Out of Their Socks
The policies intended to enforce AI detection require capabilities that neither humans nor algorithms have reliably demonstrated.
Last week, a user on X operating under the pseudonym SHL0MS posted a painting from Claude Monet’s Water Lilies series and claimed that it had been generated by AI, inviting critics to explain its inferiority to the real thing. The user reinforced the deception with X’s “Made with AI” label, and the post accumulated more than 5.5 million views before the reveal that hundreds of respondents had been confidently critiquing a genuine Monet. The prank landed in the middle of an expanding apparatus of AI-detection tools, marketplace bans, and proposed legislation, all built on the assumption that AI-generated content can be reliably distinguished from human-made work.
Loud, confident, and easily tricked
Critics responded with specific and assured analyses, including an 850-word breakdown of the painting’s supposed shortcomings and a set of eye-tracking diagrams illustrating its purportedly incoherent composition. Several respondents invoked the specialized vocabulary of AI-image criticism, one describing reflections as “noise splattered” and another lamenting that the painting “lacks the mess of humanity”, to explain defects in a painting completed more than a century ago. The analyses were detailed, technically specific, and grounded in a false premise.
Respondents with relevant expertise identified the painting correctly. Oil painter Kendric Tonn noted a clear spatial plane with lily pads and an inverted space with willow reflections, with paint texture consistent with a physical object rather than a digital generation. Art historian A.V. Marraccini identified it as a detail from an actual late Monet, recognizing the characteristic impasto and color perception changes of the artist’s cataract-affected later years. As the reveal spread, many of the most vocal critics deleted their replies.
A 2024 study published in Nature found that participants preferred AI-generated artworks over human-made ones when unaware of the source and rated those same works lower after learning that AI had produced them. The effort heuristic, documented in a 2004 study by Kruger and colleagues, demonstrated that perceived labor invested in a work increases its perceived quality independent of observable characteristics. In both cases, knowledge of origin overrode direct visual assessment.
The tools are no better
Independent benchmarks conducted in 2026 tested leading AI image detectors against outputs from Midjourney, DALL-E 3, and Stable Diffusion, with accuracy scores ranging from 78% to 96% depending on the tool and the source model. Hive Moderation scored highest at 94% across three generators in that evaluation. Winston AI, despite marketing claims of 99.98% accuracy, passed only 3 of 10 tests in a separate independent evaluation. Vendor-reported accuracy consistently exceeds independently measured results across the market.
Real-world conditions degrade these numbers further. The pixel-level artifacts and frequency-domain signatures that detectors analyze degrade with compression, resizing, screenshot capture, and post-processing, operations that are standard in social media circulation. A detector returning a high confidence score on an uncompressed original may produce an ambiguous or incorrect result after a platform’s upload pipeline has processed the same image.
Text detection exhibits parallel fragility. Vanderbilt University publicly disabled Turnitin’s AI-detection feature after excessive false positives on student writing. A Stanford study found that several widely used detectors flagged non-native English speakers as AI-generated at significantly higher rates than native speakers, including on text that those participants had written by hand. A tool that disproportionately flags legitimate work by non-native speakers compounds an accuracy problem with a discrimination problem.
Human creators are absorbing the cost of these failures. In 2022, digital artist Ben Moran was banned from Reddit’s r/Art community after moderators determined that his Photoshop illustration looked “so obviously an AI-prompted design that it doesn’t matter,” a judgment they maintained after Moran offered the original working file for review. Online marketplaces including Etsy auto-suspend listings flagged as AI-generated, with appeal processes that take days to resolve.
Regulation vs. reality
Institutions have moved from debate to enforcement. San Diego Comic-Con banned AI art from its 2026 Art Show in January, reversing a prior policy that had allowed labeled AI submissions. Paris Photo 2026 rejected “detectable AI” from its exhibitions, and similar exclusions have proliferated across art fairs, galleries, and creative marketplaces. Each exclusion requires categorizing a work as either AI-generated or human-made, a classification that neither human critics nor algorithmic tools have shown the capacity to perform reliably.
Legislative frameworks extend the same assumption into statute. California’s AB-412, the AI Copyright Transparency Act, requires disclosure of AI-generated content in commercial contexts. Federal efforts including the TRAIN Act impose transparency obligations on AI developers regarding training data. Enforcement of these disclosure requirements presupposes that nondisclosure can be detected, that a regulator or court can determine after the fact whether a work involved AI generation. The Supreme Court’s March 2026 decision declining Thaler v. Perlmutter reinforced that copyright requires human authorship but left unaddressed how that authorship can be proven when detection methods remain unreliable.