Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using graph structures based on the relational inductive bias (homophily assumption).

Explainability in artificial intelligence is crucial for restoring trust, particularly in areas like face forgery detection, where viewers often struggle to distinguish between real and fabricated content.

In this section, we provide supplementary details of our VRDP1. First, we give more details of ourphysicsmodel andtheneuro-symbolic operations intheprogram executor.

In classical compression codecs, thedecoder has to follow a well-specified procedure to ensure interoperability between different implementations of the same codec.

Inlight ofthis, weproposeanewtraining algorithm called PDE that efficiently enhances the model's robustness for a better worst-group performance.