January 4, 2026
Poison or placebo?
Nightshade: Make images unsuitable for model training
Artists plan to “poison” AI, commenters ask: bold move or déjà vu
TLDR: Nightshade claims to “poison” images so AI models trained on stolen art learn the wrong things, nudging companies to pay for licenses. Commenters are split: many say it’s old news and easy to filter, while one tester says captioning models still recognized everything—sparking a fight over effectiveness and enforcement.
Nightshade promises to give AI a bellyache: it subtly tweaks images so humans see “cow in a field,” but training models learn “handbag in grass.” The goal isn’t to smash models, but to make scraping unlicensed art expensive enough that paying creators becomes cheaper. Think Glaze’s cousin: Glaze defends your style; Nightshade goes on offense as a group deterrent. Sounds spicy, right? The community’s reaction: cue the eye-rolls. Multiple commenters claim it’s old news, linking to prior posts and more of the same. One summed up the vibe as: we’ve seen this… a few weeks ago… and two years ago.
Skeptics go further: can’t scrapers just detect and strip this “poison”? One commenter flatly says they’re “very skeptical,” suggesting a basic filter could nuke the effect. Another tossed in a brainy zinger, joking it’d be hilarious if this research ends up teaching us more about human vision than it hurts AI. The biggest splash, though, came from a hands-on test: a user ran Nightshade’s example through three captioning models and reported they still recognized the image’s features just fine—no handbag-cow confusion.
So the drama splits the room: creators cheer a pressure tactic against free-for-all scraping; techies ask whether it really works, whether it’s new, and whether model trainers will shrug and route around it. Poison, placebo, or just PR?
Key Points
- •Nightshade transforms images into “poison” samples to deter unauthorized generative AI model training.
- •The tool uses multi-objective optimization to minimize visible changes while altering model-perceived features.
- •Nightshade’s effects are robust to cropping, resampling, compression, smoothing, noise, screenshots, and photos of screens.
- •Nightshade differs from Glaze: Glaze is defensive against style mimicry; Nightshade is offensive to disrupt unauthorized scraping.
- •A low-intensity setting reduces visual impact, and the overall goal is to raise the cost of training on unlicensed data to encourage licensing.