The Receipt Is in the Pixels: Model Attribution After the Watermark Fantasy
TL;DR for operators Generated images may carry a more durable signature than most teams assume. Not a cute watermark. Not a metadata tag. Not a visible logo hiding in the corner like a nervous intern. A model-level statistical signature. The paper Guess the Unified Model: How Much Can We Recover from Generated Images? studies whether images produced by unified multimodal models can be attributed back to the model that generated them.1 The authors train a ConvNeXT classifier to identify the generating model from images produced by five open-source unified models, then extend part of the analysis to include two closed-source systems. The core result is blunt: attribution works surprisingly well. With 100 training images per model, accuracy is already 36% in a five-way task where chance is 20%. With 3K images per model, it reaches 93.9%. With 25K images per model, it reaches 99.9%. ...
