Integrating Abductive Reasoning with Transformer Perception

The research addresses a critical vulnerability in modern computer vision systems: the susceptibility of transformer-based perception models to adversarial perturbations. Instead of relying solely on standard fine-tuning or adversarial training, the authors propose an 'abductive fusion' approach. This method treats the outputs of multiple pre-trained perception models as premises and utilizes abductive logic to infer the most likely underlying state of the environment. By framing perception as an inference problem, the system can reconcile conflicting or noisy data from different models, effectively filtering out adversarial noise that would otherwise lead to misclassification.

Enhancing Robustness Through Logical Constraints

The core innovation lies in the fusion layer, which acts as a logical bridge between raw model predictions and final decision-making. By incorporating symbolic or probabilistic abductive reasoning, the framework enforces consistency constraints that adversarial inputs typically violate. This approach provides a layer of defense that is independent of the specific architecture of the underlying perception models, allowing for a modular design where individual components can be updated or replaced without retraining the entire fusion pipeline. The result is a more resilient perception system capable of maintaining accuracy even when individual models are subjected to targeted adversarial attacks.