The rising demand for Trusted AI (TAI) underscores the need for interpretable and robust models, yet existing tools rarely support graph-structured data or integrate interpretability with security. At the same time, Graph Neural Networks (GNNs) deliver state-of-the-art performance on numerous graph tasks. We present GNN-AID (Graph Neural Network Analysis, Interpretation, and Defense), an open-source Python framework for analyzing, interpreting, and defending GNNs, addressing this critical gap. Built on PyTorch-Geometric, GNN-AID offers preloaded datasets, model libraries, flexible APIs, and a web interface for visualization and no-code model design. MLOps features further support reproducibility and experiment tracking.

As invisible image watermarking gains importance for verifying AI-generated content, consistency and reproducibility remain major challenges due to the diverse methods, datasets, attacks, and metrics.We aim to provide a flexible, extensible, and user-friendly framework that enables systematic testing of watermarking methods under various conditions.We developed WIBE, a framework with command-line interfaces and YAML configuration support, enabling users to evaluate a wide range of image watermarking algorithms on various datasets, apply configurable attack scenarios, and compute standard performance metrics. WIBE includes a library of pre-implemented methods and supports integration of new watermarking techniques, attacks, metrics, and datasets through a plugin-based architecture.WIBE enables rapid prototyping, reproducible experiments, and insightful comparison of watermarking robustness. In our demo, we present its core features, plugin extensibility, and interactive infographics, making it a practical tool for researchers and practitioners working at the intersection of AI and media integrity.Project on GitHub: https://github.com/ispras/wibeYouTube video: https://youtu.be/lbWWB1crrwk