<p>The rapid progress of generative AI, spanning GANs and diffusion models, has enabled the creation of highly realistic synthetic images, which are increasingly misused to spread propaganda and misinformation. Existing attribution techniques often assume access to the generator or require large numbers of samples, conditions rarely met in practice. We introduce a few-shot attribution framework based on tiny autoencoders that share a single decoder pretrained on natural images and learn lightweight, model-specific encoders from only a handful of samples. It yields a compact, modular solution that (i) consistently outperforms CNN/Transformer baselines, while remaining competitive with respect to state-of-the-art methods; (ii) can be easily extended to new candidate generators; (iii) remains robust under common post-processing (e.g., JPEG compression and resizing); and (iv) benefits from different entropy-based approaches which improve the patch selection criterion. We further show that the same reconstruction-error features naturally support joint detection and source attribution, making this approach practical for real-world forensic pipelines. We also validate its lightweight design through a browser-based implementation that runs locally on CPU, supporting privacy-preserving forensic analysis in resource-constrained environments.</p>