I’ve been discussing a concept with a refined LLM regarding image protection and wanted to get the community’s take on the feasibility. The Concept: Instead of using Glaze/Nightshade just to ruin the style, could we engineer a specific noise pattern (adversarial perturbation) that remains invisible to the human eye but acts as a specific instruction for AI models? The Mechanism: Inject invisible noise into the original image. When the image passes through an Upscaler or Img2Img workflow, the model interprets this noise as structural data. Result: The AI “hallucinates” a clearly visible watermark (e.g., a “COPYRIGHT” text) that wasn’t visible in the source. The Challenge: It requires high transferability across models (GANs, Diffusion, Transformers). My theory is that using an “Ensemble Attack” (optimizing the noise against an average of multiple architectures) could yield a >70% success rate, creating a “dormant virus” that only triggers when someone tries to remaster the image. Is anyone working on “forced hallucination” for copyright protection? Is the math for a targeted visual trigger too complex compared to simple noise disruption? submitted by /u/Substantial_Size_451
Originally posted by u/Substantial_Size_451 on r/ArtificialInteligence